Web Data Scraping Services Have Various Method Of Business

Magnetic or optical data removal or Data Scraping Services is a term that refers to the elimination of digital storage media. Data Scraping Services of the method varies, depending on medium and method used in the process.

Similarly, patents, models, business strategies and other confidential business information, including sensitive data, can be easily accessed by others if the data is not deleted.As I said in the beginning, Data Scraping Services methods vary depending on the storage medium. For each storage medium, there are a variety of Data Scraping Services techniques.

Optical media such as  that can be destroyed by the plastic granulating. This method does not extract information, but makes recovery almost impossible. However, removal of thin film that coats the top of the disk, scraping, sanding by hand or destroy physical data. In contrast, using the microwave, a less traditional technologies, stable and disk storage layer of the thin film is very effective for the most common cause sparks to load.

Typical modern magnetic media and hard drives, tape backup units of such media is possible, but in the face of such devices requires considerable financial investment in the plant. Acids, in particular, nitric acid, 50% concentration in the iron oxide layer to react with violence, it will be completely destroyed within a few minute. In some cases it may be a storage alternative for incineration. However, this may inadvertently expose caseinogens operator and may be restricted in certain countries.

Data Scraping Services, on the other hand, is defined by Wikipedia as "an automatic search for large stores of data for patterns of practice." In other words, you already know, and you learn things about it useful analysis.

Data Scraping Services is often accompanied by a lot of complex algorithms based on statistical methods. How do you see the data in the first place - is not. Data Scraping Services analysis, you only care about what is already there in many cases, a single-pass binary wipe (to write random zeroes and ones riding) will permanently deletes all data from the storage device to remove.

use of materials recovery.
It is for this reason that the technology has been left until last.
Data Scraping Services, screen scraping is not.
This is a great simplification, so I will work a bit.

Fast-forwarding to the web world today, screen scraping is the information relates to websites. This means that computer programs "crawl" or can "spider" through web sites, data retrieval. people, We deserved pages, text data Scraping Services, automated data collection, data extraction and web site even bloody website if we have a problem it presents some.

Data Scraping Services, on the other hand, is defined by Wikipedia as "an automatic search for large stores of data for patterns of practice." In other words, you already know, and you learn things about it useful analysis. Data Scraping Services is often accompanied by a lot of complex algorithms based on statistical methods. How do you see the data in the first place - is not. Data Scraping Services analysis, you only care about what is already there.

Source:http://www.articlesbase.com/outsourcing-articles/web-data-scraping-services-have-various-method-of-business-5594515.html

Scraping By

In his classic 1976 Chesapeake portrait, Beautiful Swimmers, William Warner described the scrape boat as "a workboat unlike any other I had ever seen on the Bay." Seeming half as wide as it was long, he said, it looked like a "a miniature battleship." There's a reason for that, of course. It's a classic case of form following function; the boat evolved for one purpose, to ply the Bay's grassy shallows for shedding blue crabs.

Said to "float on a heavy dew," scrape boats run from 26 to 30 feet long and 9 to 10 feet wide. The hull is a shallow-V deadrise that quickly flattens toward the stern, enabling the boat to pull its twin scrapes—rectangular steel frames, each with a trailing mesh bag—in knee-deep waters. The broad beam might sound ungainly, but the hull tapers toward the stern—betraying its sailboat origins. And it has a graceful sheer, flowing from a bow height of a few feet to little more than a foot above the water amidships.

And you want a low freeboard when you spend the whole day hoisting aboard scrapes, which weigh 50 pounds apiece, not including the load of sea grass and crabs that come in too. Low sides or not, there's a higher than average inci-dence of back problems among scrape boat crabbers. They spend long days bending in precisely the position back doctors say puts undue pressure on the lower back as they sort through rolls of grasses to pluck out the peelers and softies. And that alone may be why crab potting is now the far more common way of catching soft crabs.

Some people think that's good, assuming that dragging a scrape across the Bay's beleaguered grass flats must be destructive. But the smooth bar of the scrape, unlike a toothed dredge, doesn't uproot grasses. In fact, where scraping is traditional, the grass beds seem relatively resilient. I've often thought if Maryland and Virginia had stuck with scraping as the major legal way to soft-crab, overfishing might not have become a problem. Pots can be deployed everywhere and by the thousands, whereas scraping is limited to grass beds and to ground covered at three miles per hour; and even the sturdiest waterman can only pull two of them by hand. But peeler pots seem here to stay, and other soft crabbers have taken to using a single, large scrape operated from larger workboats by hydraulic power.

The bottom line is that these lovely, superbly functional expressions of Chesapeake crabbing culture now number only in the dozens, if you count working, wooden models. There are some fiberglass scrape boat hulls in service, and a Carolina skiff or two has been adapted for the task. They are functional, but have little art to them.

It is probably a sign of how fast scrape boats are going that the Smithsonian Institution recently took the lines off Darlene, a scraper worked by Morris Marsh of Smith Island, for its archives. You can see photos of scrape boats, and learn more about the 140-year old history of scraping, from Paula Johnson's fine book, The Workboats of Smith Island. Mr. Marsh, still going strong in his late 60s, is the scraper who took Warner out nearly 40 years ago when he was researching Beautiful Swimmers.

Indeed, scraping seems to win over those who master it. Marsh's father-in-law, Ed Harrison, scraped for almost 70 years, nearly wearing through the cross-planked bottom of his boat—from the inside—with decades of walking the planks, tending his scrapes. And an islander who scrapes with Marsh today, David Laird, says he is 71—one year younger than Scotty Boy, the scrape boat he took over from his dad in 1958. "I wouldn't even know how to crab in another boat," Laird says.

Soft crabs may well be caught—or farmed—a century from now on the Chesapeake; but no one will devise a way to take them so intimately and beautifully from the shallowest marsh edges and tiniest crevices in the shore as the scrapers do.

Source:http://www.articlesbase.com/culture-articles/scraping-by-1560919.html

Scraping table from any web page with R or CloudStat

Scraping table from any web page with R or CloudStat:

You need to use the data from internet, but don’t type, you can just extract or scrape them if you know the web URL.

Thanks to XML package from R. It provides amazing readHTMLtable() function.

For a study case,

I want to scrape data:

    US Airline Customer Score.
    World Top Chess Players (Men).

A. Scraping US Airline Customer Score table from

http://www.theacsi.org/index.php?option=com_content&view=article&id=147&catid=&Itemid=212&i=Airlines

Code:

airline = ‘http://www.theacsi.org/index.php?option=com_content&view=article&id=147&catid=&Itemid=212&i=Airlines’

airline.table = readHTMLTable(airline, header=T, which=1,stringsAsFactors=F)

Result:

> library(XML)

Warning message:

package "XML" was built under R version 2.14.1

> airline = "http://www.theacsi.org/index.php?option=com_content&view=article&id=147&catid=&Itemid=212&i=Airlines"
> airline.table = readHTMLTable(airline, header=T, which=1,stringsAsFactors=F)
> airline.table

                     Base-line 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10
1          Southwest        78 76 76 76 74 72 70 70 74 75 73 74 74 76 79 81 79
2         All Others        NM 70 74 70 62 67 63 64 72 74 73 74 74 75 75 77 75
3           Airlines        72 69 69 67 65 63 63 61 66 67 66 66 65 63 62 64 66
4        Continental        67 64 66 64 66 64 62 67 68 68 67 70 67 69 62 68 71
5           American        70 71 71 62 67 64 63 62 63 67 66 64 62 60 62 60 63
6             United        71 67 70 68 65 62 62 59 64 63 64 61 63 56 56 56 60
7         US Airways        72 67 66 68 65 61 62 60 63 64 62 57 62 61 54 59 62
8              Delta        77 72 67 69 65 68 66 61 66 67 67 65 64 59 60 64 62
9 Northwest Airlines        69 71 67 64 63 53 62 56 65 64 64 64 61 61 57 57 61

  11 PreviousYear%Change FirstYear%Change

1 81                 2.5              3.8
3 65                -1.5             -9.7
4 64                -9.9             -4.5
5 63                 0.0            -10.0
7 61                -1.6            -15.3
8 56                -9.7            -27.3
9  #                 N/A              N/A

>

B. Scraping World Top Chess players (Men) table from http://ratings.fide.com/top.phtml?list=men

Code:

chess = ‘http://ratings.fide.com/top.phtml?list=men’
chess.table = readHTMLTable(chess, header=T, which=5,stringsAsFactors=F)

Result:

> chess = "http://ratings.fide.com/top.phtml?list=men"
> chess.table = readHTMLTable(chess, header=T, which=5,stringsAsFactors=F)
> chess.table

     Rank                       Name Title Country Rating Games B-Year

1      1           Carlsen, Magnus    g    NOR  2835   17  1990
2      2            Aronian, Levon    g    ARM  2805   25  1982
3      3         Kramnik, Vladimir    g    RUS  2801   17  1975
4      4        Anand, Viswanathan    g    IND  2799   17  1969
5      5         Radjabov, Teimour    g    AZE  2773    9  1987
6      6          Topalov, Veselin    g    BUL  2770    9  1975
7      7          Karjakin, Sergey    g    RUS  2769   16  1990
8      8         Ivanchuk, Vassily    g    UKR  2766   16  1969
9      9     Morozevich, Alexander    g    RUS  2763    6  1977
10    10           Gashimov, Vugar    g    AZE  2761    9  1986
11    11       Grischuk, Alexander    g    RUS  2761    8  1983
12    12          Nakamura, Hikaru    g    USA  2759   17  1987
13    13            Svidler, Peter    g    RUS  2749   17  1976
14    14    Mamedyarov, Shakhriyar    g    AZE  2747    9  1985
15    15       Tomashevsky, Evgeny    g    RUS  2740    0  1987
16    16            Gelfand, Boris    g    ISR  2739    9  1968
17    17          Caruana, Fabiano    g    ITA  2736   19  1992
18    18       Nepomniachtchi, Ian    g    RUS  2735   16  1990
19    19                 Wang, Hao    g    CHN  2733    6  1989
20    20              Kamsky, Gata    g    USA  2732    0  1974
21    21  Dominguez Perez, Leinier    g    CUB  2730    6  1983
22    22         Jakovenko, Dmitry    g    RUS  2729    0  1983
23    23        Ponomariov, Ruslan    g    UKR  2727   13  1983
24    24          Vitiugov, Nikita    g    RUS  2726    1  1987
25    25            Adams, Michael    g    ENG  2724   17  1971
26    26               Leko, Peter    g    HUN  2720    9  1979
27    27            Almasi, Zoltan    g    HUN  2717    8  1976
28    28               Giri, Anish    g    NED  2714   15  1994
29    29            Le, Quang Liem    g    VIE  2714    0  1991
30    30             Navara, David    g    CZE  2712    8  1985
31    31            Shirov, Alexei    g    LAT  2710   13  1972
32    32             Polgar, Judit    g    HUN  2710    0  1976
33    33     Riazantsev, Alexander    g    RUS  2710    0  1985
34    34       Wojtaszek, Radoslaw    g    POL  2706    8  1987
35    35      Moiseenko, Alexander    g    UKR  2706    7  1980
36    36   Vallejo Pons, Francisco    g    ESP  2705   15  1982
37    37        Malakhov, Vladimir    g    RUS  2705    0  1980
38    38            Jobava, Baadur    g    GEO  2704   23  1983
39    39           Bacrot, Etienne    g    FRA  2704   14  1983
40    40          Laznicka, Viktor    g    CZE  2704    8  1988
41    41            Sutovsky, Emil    g    ISR  2703    8  1977
42    42        Naiditsch, Arkadij    g    GER  2702   14  1985
43    43         Movsesian, Sergei    g    ARM  2700    9  1978
44    44       Sasikiran, Krishnan    g    IND  2700    9  1981
45    45   Vachier-Lagrave, Maxime    g    FRA  2699   13  1990
46    46            Dreev, Aleksey    g    RUS  2698    6  1969
47    47           Efimenko, Zahar    g    UKR  2695    8  1985
48    48         Volokitin, Andrei    g    UKR  2695    0  1986
49    49                 Wang, Yue    g    CHN  2694    6  1987
50    50        Fressinet, Laurent    g    FRA  2693   17  1981
51    51                Li, Chao b    g    CHN  2693    6  1989
52    52            Grachev, Boris    g    RUS  2693    0  1986
53    53      Nielsen, Peter Heine    g    DEN  2693    0  1973
54    54            Van Wely, Loek    g    NED  2692   13  1972
55    55    Bruzon Batista, Lazaro    g    CUB  2691   19  1982
56    56           McShane, Luke J    g    ENG  2691    8  1984
57    57            Eljanov, Pavel    g    UKR  2690   10  1983
58    58      Kasimdzhanov, Rustam    g    UZB  2689   14  1979
59    59         Inarkiev, Ernesto    g    RUS  2689    6  1985
60    60         Zvjaginsev, Vadim    g    RUS  2688    8  1976
61    61         Andreikin, Dmitry    g    RUS  2688    0  1990
62    62    Areshchenko, Alexander    g    UKR  2688    0  1986
63    63         Rublevsky, Sergei    g    RUS  2686    0  1974
64    64         Akopian, Vladimir    g    ARM  2685    8  1971
65    65          Potkin, Vladimir    g    RUS  2684    0  1982
66    66       Sargissian, Gabriel    g    ARM  2683   15  1983
67    67            Berkes, Ferenc    g    HUN  2682   16  1985
68    68           Bologan, Viktor    g    MDA  2680   15  1971
69    69          Bauer, Christian    g    FRA  2679   24  1977
70    70          Tiviakov, Sergei    g    NED  2677   22  1973
71    71            Short, Nigel D    g    ENG  2677   15  1965
72    72        Motylev, Alexander    g    RUS  2677    6  1979
73    73         Gharamian, Tigran    g    FRA  2676    0  1984
74    74          Kobalia, Mikhail    g    RUS  2673    0  1978
75    75              Meier, Georg    g    GER  2671    9  1987
76    76       Onischuk, Alexander    g    USA  2670   13  1975
77    77              Bu, Xiangzhi    g    CHN  2670    6  1985
78    78          Alekseev, Evgeny    g    RUS  2670    0  1985
79    79            Azarov, Sergei    g    BLR  2667    0  1983
80    80        Kryvoruchko, Yuriy    g    UKR  2666    0  1986
81    81             Balogh, Csaba    g    HUN  2665    8  1987
82    82           Harikrishna, P.    g    IND  2665    6  1986
83    83       Khismatullin, Denis    g    RUS  2664    8  1984
84    84   Nguyen, Ngoc Truong Son    g    VIE  2662    6  1990
85    85           Fridman, Daniel    g    GER  2660   11  1976
86    86              Smirin, Ilia    g    ISR  2660    7  1968
87    87               Ding, Liren    g    CHN  2660    6  1992
88    88         Sadler, Matthew D    g    ENG  2660    3  1974
89    89            Korobov, Anton    g    UKR  2660    0  1985
90    90          Cheparinov, Ivan    g    BUL  2659   18  1986
91    91          Timofeev, Artyom    g    RUS  2659    0  1985
92    92           Georgiev, Kiril    g    BUL  2658   17  1965
93    93           Bartel, Mateusz    g    POL  2658    9  1985
94    94          Zhigalko, Sergei    g    BLR  2658    8  1989
95    95         Feller, Sebastien    g    FRA  2658    0  1991
96    96            Ragger, Markus    g    AUT  2655   17  1988
97    97         Jones, Gawain C B    g    ENG  2653   27  1987
98    98                So, Wesley    g    PHI  2653    5  1993
99    99              Milov, Vadim    g    SUI  2653    0  1972
100  100           Gupta, Abhijeet    g    IND  2652    9  1989
101  101            Postny, Evgeny    g    ISR  2652    8  1981
102  102             Roiz, Michael    g    ISR  2652    6  1983
103  103           Gyimesi, Zoltan    g    HUN  2652    4  1977
104  104          Nikolic, Predrag    g    BIH  2652    2  1960

>

Done. You had successfully scraping data from any web page with R or CloudStat.

Then, you can analyze as usual! Great! No more retype the data. Enjoy!

Source: http://www.r-bloggers.com/scraping-table-from-any-web-page-with-r-or-cloudstat/

Extracting Wisdom Teeth Tips

It is believed that due to evolution, our jaws are now smaller than our ancient ancestors'. For this reason, our mouths often do not have adequate room to accommodate the third molars, making them basically useless and in some cases detrimental. Even if they are not impacted, wisdom teeth may be hard to clean, and therefore require removal to reduce the probability of caries and infection.

As part of your routine dental visits, your dentist will likely take X-rays to monitor the development of your third molars. Your dentist will likely recommend removing them as soon as possible to avoid any complications. The extraction of wisdom teeth can sometimes be a costly and daunting procedure; for these reasons many patients delay having them extracted. However, if the impacted teeth become infected, it is important to see your dental professional at once. Symptoms of infection due to impacted wisdom teeth include;

•    Pain in the gums and surrounding areas
•    Red or inflamed gums
•    Tender or bleeding gums
•    Inflammation around the face and jaw
•    Bad breath (halitosis)
•    Frequent headaches

If a single molar needs to be extracted, local anesthetic will be used. In the case where several or all the teeth need extraction, the patient will usually be "put under" using a general anesthetic. If you have an infection or medical complications that put you at a higher than normal risk, the surgery may be performed at a hospital. Extraction of the wisdom teeth is a day surgery, and patients are usually able to return to normal activities in a day or so. You may be prescribed antibiotics prior to the surgery, and you will likely be asked not to eat or drink the night before the surgery.

During the surgery, your dentist makes an incision in the gum tissue covering the tooth. Once the tooth is exposed, the dentist may cut the tooth into smaller pieces to make extraction easier. After the extraction you will be given stitches to mend the gum tissue. You may need to return a few days later to have the stitches removed. You will be monitored after the surgery to ensure that you are not bleeding excessively.

The best time for extraction is when the patient is in their late teens to avoid unnecessary complications. Wisdom teeth extractions performed later in life are still beneficial, but the removal may be more difficult and healing may take longer. Therefore it is wise to have a conversation with your dentist regarding your wisdom teeth as early as possible.

Most people will experience the emergence of their wisdom teeth at some point in their life, and extraction is sometimes necessary as a preventative measure or to fix an actual problem or to prevent problem. It is best to deal with any problems regarding your wisdom teeth as soon as possible to avoid unnecessary difficulties.

Source:http://ezinearticles.com/?Extracting-Wisdom-Teeth-Tips&id=7788863

Importance of Data Mining Services in Business

Data mining is used in re-establishment of hidden information of the data of the algorithms. It helps to extract the useful information starting from the data, which can be useful to make practical interpretations for the decision making.

It can be technically defined as automated extraction of hidden information of great databases for the predictive analysis. In other words, it is the retrieval of useful information from large masses of data, which is also presented in an analyzed form for specific decision-making. Although data mining is a relatively new term, the technology is not. It is thus also known as Knowledge discovery in databases since it grip searching for implied information in large databases.

It is primarily used today by companies with a strong customer focus - retail, financial, communication and marketing organizations. It is having lot of importance because of its huge applicability. It is being used increasingly in business applications for understanding and then predicting valuable data, like consumer buying actions and buying tendency, profiles of customers, industry analysis, etc. It is used in several applications like market research, consumer behavior, direct marketing, bioinformatics, genetics, text analysis, e-commerce, customer relationship management and financial services.

However, the use of some advanced technologies makes it a decision making tool as well. It is used in market research, industry research and for competitor analysis. It has applications in major industries like direct marketing, e-commerce, customer relationship management, scientific tests, genetics, financial services and utilities.

Data mining consists of major elements:

•    Extract and load operation data onto the data store system.
•    Store and manage the data in a multidimensional database system.
•    Provide data access to business analysts and information technology professionals.
•    Analyze the data by application software.
•    Present the data in a useful format, such as a graph or table.

The use of data mining in business makes the data more related in application. There are several kinds of data mining: text mining, web mining, relational databases, graphic data mining, audio mining and video mining, which are all used in business intelligence applications. Data mining software is used to analyze consumer data and trends in banking as well as many other industries.

Source:http://ezinearticles.com/?Importance-of-Data-Mining-Services-in-Business&id=2601221

Autoscraping casts a wider net

We have recently started letting more users into the private beta for our Autoscraping service. We’re receiving a lot of applications following the shutdown of Needlebase and we’re increasing our capacity to accommodate these users.

Natalia made a screencast to help our new users get started:

It’s also a great introduction to what this service can do.

We released slybot as an open source integration of the scrapely extraction library and the scrapy framework. This is the core technology behind the autoscraping service and we will make it easy to export autoscraping spiders from Scrapinghub  and run them completely with slybot – allowing our users to have the flexibility and freedom provided by open source.

Source:http://blog.scrapinghub.com/2012/02/27/autoscraping-casts-a-wider-net/

ScraperWiki: A story about two boys, web scraping and a worm

“It’s like a buddy movie.” she said.
Not quite the kind of story lead I’m used to. But what do you expect if you employ journalists in a tech startup?
“Tell them about that computer game of his that you bought with your pocket money.”
She means the one with the risqué name.
I think I’d rather tell you about screen scraping, and why it is fundamental to the nature of data.

About how Julian spent almost a decade scraping himself to death until deciding to step back out and build a tool to make it easier.

I’ll give one example.
Two boys
In 2003, Julian wanted to know how his MP had voted on the Iraq war.
The lists of votes were there, on the www.parliament.uk website. But buried behind dozens of mouse clicks.
Julian and I wrote some software to read the pages for us, and created what eventually became TheyWorkForYou.

We could slice and dice the votes, mix them with some knowledge from political anaroks, and create simple sentences. Mini computer generated stories.

“Louise Ellman voted very strongly for the Iraq war.”
You can see it, and other stories, there now. Try the postcode of the ScraperWiki office, L3 5RF.

I remember the first lobbiest I showed it to. She couldn’t believe it. Decades of work done in an instant by a computer. An encyclopedia of data there in a moment.

Web Scraping

It might seem like a trick at first, as if it was special to Parliament. But actually, everyone does this kind of thing.

Google search is just a giant screen scraper, with one secret sauce algorithm guessing its ranking data.
Facebook uses scraping as a core part of its viral growth to let users easily import their email address book.

There’s lots of messy data in the world. Talk to a geek or a tech company, and you’ll find a screen scraper somewhere.

Why is this?
It’s Tautology

On the surface, screen scrapers look just like devices to work round incomplete IT systems.

Parliament used to publish quite rough HTML, and certainly had no database of MP voting records. So yes, scrapers are partly a clever trick to get round that.

But even if Parliament had published it in a structured format, their publishing would never have been quite right for what we wanted to do.

We still would have had to write a data loader (search for ‘ETL’ to see what a big industry that is). We still would have had to refine the data, linking to other datasets we used about MPs. We still would have had to validate it, like when we found the dead MP who voted.

It would have needed quite a bit of programming, that would have looked very much like a screen scraper.

And then, of course, we still would have had to build the application, connecting the data to the code that delivered the tool that millions of wonks and citizens use every year.

Core to it all is this: When you’re reusing data for a new purpose, a purpose the original creator didn’t intend, you have to work at it.

Put like that, it’s a tautology.
A journalist doesn’t just want to know what the person who created the data wanted them to know.
Scrape Through
So when Julian asked me to be CEO of ScraperWiki, that’s what went through my head.
Secrets buried everywhere.

The same kind of benefits we found for politics in TheyWorkForYou, but scattered across a hundred countries of public data, buried in a thousand corporate intranets.

If only there was a tool for that.
A Worm
And what about my pocket money?
Nicola was talking about Fat Worm Blows a Sparky.
Julian’s boss’s wife gave it its risqué name while blowing bubbles in the bath. It was 1986. Computers were new. He was 17.

Fat Worm cost me £9.95. I was 12.
[Loading screen]
I was on at most £1 a week, so that was ten weeks of savings.
Luckily, the 3D graphics were incomprehensibly good for the mid 1980s. Wonder who the genius programmer is.
I hadn’t met him yet, but it was the start of this story.

Source:https://blog.scraperwiki.com/2011/05/scraperwiki-a-story-about-two-boys-web-scraping-and-a-worm/

Seven tools for web scraping – To use for data journalism & creating insightful content

I’ve been creating a lot of (data driven) creative content lately and one of the things I like to do is gathering as much data as I can from public sources. I even have some cases it is costing to much time to create and run database queries and my personal build PHP scraper is faster so I just wanted to share some tools that could be helpful. Just a short disclaimer: use these tools on your own risk! Scraping websites could generate high numbers of pageviews and with that, using bandwidth from the website you are scraping.

1. Scraper (Chrome plugin)

    Scraper is a simple data mining extension for Google Chrome™ that is useful for online research when you need to quickly analyze data in spreadsheet form.

You can select a specific data point, a price, a rating etc and then use your browser menu: click Scrape Similar and you will get multiple options to export or copy your data to Excel or Google Docs. This plugin is really basic but does the job it is build for: fast and easy screen scraping.

2. Simple PHP Scraper

PHP has a DOMXpath function. I’m not going to explain how this function works, but with the script below you can easily scrape a list of URLs. Since it is PHP, use a cronjob to hourly, daily or weekly scrape the desired data. If you are not used to creating Xpath references, use the Scraper for Chrome plugin by selecting the data point and see the Xpath reference directly.

scraper-example

– Click here to download the example script.

3. Kimono Labs

Kimono has two easy ways to scrape specific URLs: just paste the URL into their website or use their bookmark. Once you have pointed out the data you need, you can set how often and when you want the data to be collected. The data is saved in their database. I like the facts that their learning curve is not that steep and it doesn’t look like you need a PHD in engineering to use their software. The disadvantage of this tool is the fact you can’t upload multiple URLs at once.

4. Import.io

Import.io is a browser based web scraping tool. By following their easy step-by-step plan you select the data you want to scrape and the tool does the rest. It is a more sophisticated tool compared to Kimono. I like it because of the fact it shows a clear overview of all the scrapers you have active and you can scrape multiple URLs at once.

5. Outwit Hub

I will start with the two biggest differences compared to the previous tool: it is a softwarepackage to use on your PC or laptop and to use its full potential it will cost you 75 USD. The free version can only scrape 100 rows of data. What I do like is the number of preprogrammed options to scrape which makes it easy to start and learn about web scraping.

6. ScraperWiki

This tool is really for people wanting to scrape on a massive scale. You can code your own scrapers (in PHP, Ruby & Python) and pricing is really cheap looking to what you can get: 29USD / month for 100 datasets. You are completely free in using libraries and timers. And if your programming skills are not good enough, they can help you out (paid service though). Compared to other tools, this is the most advanced tool that offers the basics of web scraping.

7. Fminer.com

This tool made it possible to finally scrape all the data inside Google Webmaster Tools since it can deal with JavaScript and AJAX interfaces. Read my extensive review on this page: Scraping Webmaster Tools with FMiner!

But on the end, building your individual project scrapers will always be more effective than using predefined scrapers. Am I missing any tools in this sum up of tools?

Source: http://www.notprovided.eu/7-tools-web-scraping-use-data-journalism-creating-insightful-content/

Web Data Extraction: driving data your way

Most businesses rely on the web to gather data such as product specifications, pricing information, market trends, competitor information, and regulatory details. More often than not, companies collect this data manually—a process that not only takes a significant amount of time, but also has the potential to introduce costly errors.

By automating data extraction, you're able to free yourself (and your pointer finger) from hours of copy/pasting, eliminate human errors, and focus on the parts of your job that make you feel great.

Web data extraction: What it is, why it's used, and how to get it right on an ongoing basis

Web data extraction, screen scraping, web harvesting—while these terms may have different connotations they all essentially point to the same thing: plucking data from the web and populating it in an organized way in another place for further analysis or more focused use. In an era where “big data” has become a commonplace concept, the appeal of web data extraction has grown; it’s an extremely efficient alternative to web browsing, and culls very specific data for a focused purpose.

How it's used

While each company’s needs vary, data extraction is often used for:

    Competitive intelligence, including web popularity, social perception, other sites linking to them, and placement of competitor advertisements

    Gathering financial data including stock market movement, product pricing, and more

    Creating continuity between price sheets and online websites, catalogs, or inventory databases

    Capturing product specifications like dimensions, color, and materials

    Pulling tabular data from multiple sources for in-depth analysis

Interestingly, some people even find that web data extraction can aid them in their leisure time as well, pulling data from blogs and websites that pertain to their hobbies or interests, and creating their own library of organized information on a topic. Perhaps, for instance, you want a list of all the designers that George Clooney wears (hey- we won’t question what you do in your free time). By using web scraping tools, you could automatically extract this type of data from, say, a fashion blogger who follows celebrity style, and create your own up-to-date shopping list of items.

How it's done

When you think of gathering data from the web, you should mentally juxtapose two different images: one of gathering a bucket of sand one grain at a time, and one of filling a bucket with a shovel that has the perfect scoop size to fill it completely in one sitting. While clearly the second method makes the most sense, the majority of web data extraction happens much like the first process--manually, and slowly.

Let’s take a look at a few different ways organizations extract data today.

The least productive way: manually

While this method is the least efficient, it’s also the most widespread. On the plus side, you need to learn absolutely nothing except “Ctrl+C/V” to use this method, which explains why it is the generally preferred method, despite the hours of time it can take. Imagine, for instance, managing a sales spreadsheet that keeps inventory up to date so that the information can be properly disseminated to a global sales team. Not only does it take a significant amount of time to update the spreadsheet with information from, say, your internal database and manufacturer’s website, but information may change rapidly, leaving sales reps with inaccurate information regardless.

Finding someone in the organization with a talent for programming languages like Python

Generally, automating a task without dedicated automation software requires programming, and therefore an internal resource with a solid familiarity with programming languages to create the task and corresponding script. While most organizations do, in fact, have a resource in IT or engineering with this type of ability, it often doesn’t seem like a worthy time investment for that person to derail the initiatives he or she is working on to automate web data extraction. Additionally, if companies do choose to automate using in-house resources, that person will find himself beholden to a continuing obligation, since he or she will need to adjust scripting if web objects and attributes change, disabling the task.

Outsourcing via Elance or oDesk

Unless there is a dedicated resource ready to automate and maintain data extraction processes (and most organizations wouldn’t necessarily choose to use their in-house employee time this way), companies might turn to outsourcing companies such as Elance or oDesk to hire contract help. While this is an effective way to automate a task using a resource that has a level of acumen in automation, it represents an additional cost--be it one time or on a regular basis as data extraction requirements change or increase.

Using Excel web queries

Since more often than not, data extracted from the web is often populated into an Excel spreadsheet, it’s no wonder that Excel includes web query tools expressly for that purpose. These tools are particularly useful in pulling tabular data from a website (such as product specifications, legal codes, stock prices, and a host of other information) and automatically pushing the data into a spreadsheet. Excel queries do have limitations and a learning curve, however, particularly when creating dynamic web queries. And clearly, if you’re hoping to populate the information in other sources, such as external databases, there is yet another level of difficulty to navigate.

How automation simplifies web data extraction

Culling web data quickly

Using automation is the simplest way to extract web data. As you execute the steps necessary to perform the task one time, a macro recorder captures each action, automatically generates an easily-editable script, and lets you specify how often you would like to repeat the task, and at what speed.

Maintaining the highest level of accuracy

With humans copy/pasting data, or comparing between multiple screens and entering data manually into a spreadsheet, you’re likely to run into accuracy issues (sometimes directly proportionate to the amount of time spent on the task and amount of coffee in the office!) Automation software ensures that “what you see is what you get,” and that data is picked up from the web and put back down where you want it without a hitch.

Storing web data in your preferred format

Not only can you accurately transfer data with automation software, you can also ensure that it’s populated into spreadsheets or databases in the format you prefer. Rather than simply dumping the data into a spreadsheet, you can ensure that the right information is put into the proper column, row, field, and style (think, for instance, of the difference between writing a birth date as “03/13/1912” and “12/3/13”).

Simplifying data analysis

Automation software allows you to aggregate data from disparate sources or enormous stockpiles of structured or unstructured data in a way that makes sense for your business analysis needs. This way, the majority of employees in an organization can perform some level of analysis on their own, making it easier to surface information that informs business decisions.

Reacting to changes without a hitch

Because automation software is built to recognize icons, images, symbols, and other objects regardless of their position on a screen, it can automate processes in a self-perpetuating manner. For example, let’s say you automate data retrieval from a certain chart on a retailer’s website without automation software. If the retailer decides to move that object to another area of the screen, your task would no longer produce accurate results (or work at all), leaving you to make changes to the script (or find someone who can), or re-record the task altogether. With image recognition capabilities, however, the system “memorizes” the object itself, not merely its coordinates, so that the task can continue to run irrespective of changes.

The wide sweeping appeal of automation software

Companies often pick a comprehensive automation solution not only because of its ability to effectively automate any web data extraction task, but also because it goes beyond data extraction. Automation software can permeate into other areas of the business as well, making tasks such as application integration, data migration, IT processes, Excel automation, testing, and routine tasks such as launching applications or formatting files faster and more accurate. Because it requires no programming experience to use, adoption rates are higher and businesses get more “bang for their buck.”

Almost any organization can benefit from using automation software, particularly as they grow and scale. If you are looking to quit “moving grains of sand” and start claiming back time in your day, there are a few steps you can take:

 Watch a short video that shows how web data extraction is done with automation software

 Download a free trial and start reaping the benefits of downloading even just a couple of tasks today.

 See how tasks are automated with our short, step-by-step how-to-sheets (and then give it a try yourself!)

Source: https://www.automationanywhere.com/web-data-extraction

Outsourcing Data Mining is a Wise Business Decision

Most businesses nowadays have a large volume of raw data that is never processed, because of the lack of time or resources. If your business is facing a similar situation, then you are missing out on valuable information. Without the right information, your company will be unable to make accurate business decisions.

The right information can play a key role in promoting the growth of your business. When unprocessed data is entered, filtered, classified and converted into a workable format, it can be used to maximize your profits, ameliorate your risks and run a seamless workflow.

Over the years, data mining has proved to be extremely useful in various industries, be it, healthcare, direct marketing, e-commerce, finance, customer relationship management or telecommunications. With the right information, companies have been able to make fast and effective business decisions.

Why outsource data mining?

Data mining requires the expertise of professional business and financial analysts who understand how to acquire important information from vast amounts of data. If data mining is done in-house, it can become expensive and time consuming. It can also shift your focus away from core business activities. Outsourcing data mining on the other hand is more fast, cost-effective and can give you access to professional services.

4 commonly outsourced data mining functions

Most companies outsource one or more of the following data mining functions to India:

1. Data congregation: Data is extracted from various web pages and websites, by using methods like web and screen scraping. The collected data is then entered into a database.

2. Contact data collection: Different websites are searched and information concerning contacts is collected.

3. E-commerce data: Data about varied online stores are collected, taking into account information about prices, discounts and products.

4. Data about competitors: Data about business competitors are collected to help a company gauge itself against its competition. With such valuable data, you can effectively re-design your marketing strategy and pricing matrix.

8 advantages of outsourcing data mining to India

With data mining out of your hands, your business can make huge savings in terms of time, money and infrastructure. The following are some of the benefits that you can leverage by outsourcing data mining to India:

    Get qualified and highly skilled data mining experts to work for you at an extremely affordable cost

    Be assured of the quality of information, as Indian data entry companies only extract information from reliable websites and databases

    Save on the cost of investing on the latest data mining software and technology, as your Indian service provider will be making these investments

    Get your data processed within a short turnaround time of 3,6 or 12 hours as Indian data mining companies can provide efficient data mining within a few hours

    When compared to in-house data mining, outsourcing data mining can be a lot cheaper and also bring you better results

    Stay assured about the complete privacy, security and confidentiality of your valuable data as Indian data mining companies use the latest technology to ensure 100% safety

    Get access to data with a wide market coverage as your Indian data mining provider will be serving many business with varied data mining needs

    Improve your overall productivity and generate more profits by making informed decisions about your business

Have you outsourced data mining before? If yes, which data mining service did you outsource? Did you find outsourcing more advantageous that in-house data mining. Let us know.

Source: http://blog.flatworldsolutions.com/outsourcing-data-mining-is-a-wise-business-decision/

Online Data Entry & Web Scraping Services

To operate any type of organization smoothly, it is essential to have precise data that is accurate and reliable. When your business expands, data entry on an ongoing basis is a tedious job. It’s a very time consuming task that can often distract employees focusing on core business areas.

Webpop offers all forms of online data entry services that are quick and accurate. We provide data entry services across all verticals that can be completely customized to your business requirements.

Database Population Services

Database population involves content collection from various database sources. This requires a lot of attention to detail, dedication and awareness and can prove a formidable task, especially for websites that largeley depend on it.

Webpop offer a quick and efficient database population service that helps relieve the stress from an extremely laborius task and leaves you more time to focus on more important aspects of your business. By investing just a fraction of the cost, you can outsource your database population tasks to us.

Web Scraping Services

Webpop have been assisting clients in searching, extracting and collecting data from the web for the past 5 years using the latest techniques in web scraping techology. We can scrape all types of information from a variety of sources such as websites, blogs, online directories, e-commerce websites and podcasts to name a few. We use a varied selection of automated and manual web scraping technologies to extract, gather and collect all of the required data you require from any chosen website(s) on the World Wide Web.

We can simplify the whole process from collection to population, converting your scraped data in to structured formats that are applicable to your website. This can be offered as a one time service or an ongoing basis that will assist you in constantly keeping your website’s content fresh and up to date. We can crawl competitors websites, gather sales leads, product details, pricing methodologies and also creat custom campaigns to suit your project’s requirements.

Over the years Webpop has grown from strength-to-strength by providing all types of data entry, database population and web scraping services. All of our data entry services are performed with care, due dilligence and attention to detail. We enjoy a challenge and pride ourselves on delivering results whilst working on precarious projects that require precision and total commitment.

Source:http://www.webpopdesign.com/services/data-entry/

Kimono Is A Smarter Web Scraper That Lets You “API-ify” The Web, No Code Required

A new Y Combinator-backed startup called Kimono wants to make it easier to access data from the unstructured web with a point-and-click tool that can extract information from webpages that don’t have an API available. And for non-developers, Kimono plans to eventually allow anyone track data without needing to understand APIs at all.

This sort of smarter “web scraper” idea has been tried before, and has always struggled to find more than a niche audience. Previous attempts with similar services like Dapper or Needlebase, for example, folded. Yahoo Pipes still chugs along, but it’s fair to say that the service has long since been a priority for its parent company.

But Kimono’s founders believe that the issue at hand is largely timing.

“Companies more and more are realizing there’s a lot of value in opening up some of their data sets via APIs to allow developers to build these ecosystems of interesting apps and visualizations that people will share and drive up awareness of the company,” says Kimono co-founder Pratap Ranade. (He also delves into this subject deeper in a Forbes piece here). But often, companies don’t know how to begin in terms of what data to open up, or how. Kimono could inform them.

Plus, adds Ranade, Kimono is materially different from earlier efforts like Dapper or Needlebase, because it’s outputting to APIs and is starting off by focusing on the developer user base, with an expansion to non-technical users planned for the future. (Meanwhile, older competitors were often the other way around).

The company itself is only a month old, and was built by former Columbia grad school companions Ranade and Ryan Rowe. Both left grad school to work elsewhere, with Rowe off to Frog Design and Ranade at McKinsey. But over the nearly half-dozen or so years they continued their careers paths separately, the two stayed in touch and worked on various small projects together.

One of those was Airpapa.com, a website that told you which movies were showing on your flights. This ended up giving them the idea for Kimono, as it turned out. To get the data they needed for the site, they had to scrape data from several publicly available websites.

“The whole process of cleaning that [data] up, extracting it on a schedule…it was kind of a painful process,” explains Rowe. “We spent most of our time doing that, and very little time building the website itself,” he says. At the same time, while Rowe was at Frog, he realized that the company had a lot of non-technical designers who needed access to data to make interesting design decisions, but who weren’t equipped to go out and get the data for themselves.

With Kimono, the end goal is to simplify data extraction so that anyone can manage it. After signing up, you install a bookmarklet in your browser, which, when clicked, puts the website into a special state that allows you to point to the items you want to track. For example, if you were trying to track movie times, you might click on the movie titles and showtimes. Then Kimono’s learning algorithm will build a data model involving the items you’ve selected.

Screen Shot 2014-02-18 at 4.29.05 PM

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That data can be tracked in real time and extracted in a variety of ways, including to Excel as a .CSV file, to RSS in the form of email alerts, or for developers as a RESTful API that returns JSON. Kimono also offers “Kimonoblocks,” which lets you drop the data as an embed on a webpage, and it offers a simple mobile app builder, which lets you turn the data into a mobile web application.

Screen Shot 2014-02-18 at 4.29.50 PM

For developer users, the company is currently working on an API editor, which would allow you to combine multiple APIs into one.

So far, the team says, they’ve been “very pleasantly surprised” by the number of sign-ups, which have reached ten thousand*. And even though only a month old, they’ve seen active users in the thousands.

Initially, they’ve found traction with hardware hackers who have done fun things like making an airhorn blow every time someone funds their Kickstarter campaign, for instance, as well as with those who have used Kimono for visualization purposes, or monitoring the exchange rates of various cryptocurrencies like Bitcoin and dogecoin. Others still are monitoring data that’s later spit back out as a Twitter bot.

Kimono APIs are now making over 100,000 calls every week, and usage is growing by over 50 percent per week. The company also put out an unofficial “Sochi Olympics API” to showcase what the platform can do.

The current business model is freemium based, with pricing that kicks in for higher-frequency usage at scale.

The Mountain View-based company is a team of just the two founders for now, and has initial investment from YC, YC VC and SV Angel.

Source:http://techcrunch.com/2014/02/18/kimono-is-a-smarter-web-scraper-that-lets-you-api-ify-the-web-no-code-required/

A Web Scraper’s Guide to Kimono

Being a frequent reader of Hacker News, I noticed an item on the front page earlier this year which read, “Kimono – Never write a web scraper again.” Although it got a great number of upvotes, the tech junta was quick to note issues, especially if you are a developer who knows how to write scrapers. The biggest concern was a non-intuitive UX, followed by the inability of the first beta version to extract data items from websites as smoothly as the demo video suggested.

I decided to give it a few months before I tested it out, and I finally got the chance to do so recently.

Kimono is a Y-Combinator backed startup trying to do something in a field where others have failed. Kimono is focused on creating APIs for websites which don’t have one, another term would be web scraping. Imagine you have a website which shows some data you would like to dynamically process in your website or application. If the website doesn’t have an API, you can create one using Kimono by extracting the data items from the website.

Is it Legal?

Kimono provides an FAQ section, which says that web scraping from public websites “is 100% legal” as long as you check the robots.txt file to see which URL patterns they have disallowed. However, I would advise you to proceed with caution because some websites can pose a problem.

A robots.txt is a file that gives directions to crawlers (usually of search engines) visiting the website. If a webmaster wants a page to be available on search engines like Google, he would not disallow robots in the robots.txt file. If they’d prefer no one scrapes their content, they’d specifically mention it in their Terms of Service. You should always look at the terms before creating an API through Kimono.

An example of this is Medium. Their robots.txt file doesn’t mention anything about their public posts, but the following quote from their TOS page shows you shouldn’t scrape them (since it involves extracting data from their HTML/CSS).

    For the remainder of the site, you may not duplicate, copy, or reuse any portion of the HTML/CSS, JavaScipt, logos, or visual design elements without express written permission from Medium unless otherwise permitted by law.

If you check the #BuiltWithKimono section of their website, you’d notice a few straightforward applications. For instance, there is a price comparison API, which is built by extracting the prices from product pages on different websites.

Let us move on and see how we can use this service.

What are we about to do?

Let’s try to accomplish a task, while exploring Kimono. The Blog Bowl is a blog directory where you can share and discover blogs. The posts that have been shared by users are available on the feeds page. Let us try to get a list of blog posts from the page.

The simple thought process when scraping the data is parsing the HTML (or searching through it, in simpler terms) and extracting the information we require. In this case, let’s try to get the title of the post, its link, and the blogger’s name and profile page.

Source: http://www.sitepoint.com/web-scrapers-guide-kimono/

Future of Web Scraping

The Internet is large, complex and ever-evolving. Nearly 90% of all the data in the world has been generated over the last two years. In this vast ocean of data, how does one get to the relevant piece of information? This is where web scraping takes over.

Web scrapers attach themselves, like a leech, to this beast and ride the waves by extracting information form websites at will. Granted “scraping” doesn’t have a lot of positive connotations, yet it happens to be the only way to access data or content from a web site without RSS or an open API.

Future of Web Scraping

Web scraping faces testing times ahead. We outline why there may be some serious challenges to its future.

With rise in data, redundancies in web scraping are rising. No more is web scraping a domain of the coders; in fact, companies now offer customized scraping tools to clients which they can use to get the data they want. The outcome of everyone equipped to crawl, scrape, and extract, is unnecessary waste of precious man-power. Collaborative scraping could well heal this hurt. Here, where one web crawler does a broad scraping, the others scrape data off an API. An extension of the problem is that text retrieval attracts more attention than multimedia; and with websites becoming more complex, this enforces limited scraping capacity.

Easily, the biggest challenge to web scraping technology is Privacy concerns. With data freely available (most of it voluntary, much of it involuntary), the call for stricter legislation rings loudest. Unintended users can easily target a company and take advantage of the business using web scraping. The disdain with which “do not scrape” policies are treated and terms of usage violated, tells us that even legal restrictions are not enough. This begs to ask an age-old question: is scraping legal?

Is Crawling Legal? from PromptCloud

The flipside to this argument is that if technological barriers replace legal clauses, then web scraping will see a steady, and sure, decline. This is a distinct possibility since the only way scraping activity thrives is on the grid, and if the very means are taken away and programs no longer have access to website information, then web scraping by itself will be wiped out.

Building the Future

On the same thought is the growing trend of accepting “open data”. The open data policy, while long mused hasn’t been used at the scale it should be. The old way was to believe that closed data is the edge over competitors. But that mindset is changing. Increasingly, websites are beginning to offer APIs and embracing open data. But what’s the advantage of doing so?

Selling APIs not only brings in the money, but also is useful in driving back traffic to the sites! APIs are also a more controlled, cleaner way of turning sites into services. Steadily many successful sites like Twitter, LinkedIn etc. are offering access to their APIs with paid services and actively blocking scraper and bots.

Yet, beyond these obvious challenges, there’s a glimmer of hope for web scraping. And this is based on a singular factor: the growing need for data!

With Internet & web technology spreading, massive amounts of data will be accessible on the web. Particularly with increased adoption of mobile internet. According to one report, by 2020, the number of mobile internet users will hit 3.8 billion, or around half of the world’s population!

Since ‘big data’ can be both, structured & unstructured; web scraping tools will only get sharper and incisive. There is fierce competition between those who provide web scraping solutions. With the rise of open source languages like Python, R & Ruby, Customized scraping tools will only flourish bringing in a new wave of data collection and aggregation methods.

Source: https://www.promptcloud.com/blog/Future-of-Web-Scraping

Big Data Democratization via Web Scraping

Big Data Democratization via Web Scraping

If  we had to put democratization of data inline with the classroom definition of democracy, it would read- Data by the people, for the people, of the people. Makes a lot of sense, doesn’t it? It resonates with the generic feeling we have these days with respect to easy access to data for our daily tasks. Thanks to the internet revolution, and now the social media.

Big-data-crawling

Big Data web Crawling

By the people- most of the public data on the web is a user group’s sentiments, analyses and other information.

Of the people- Although the “of” here does not literally mean that the data is owned, all such data on the internet either relates to the user group itself or its views on things.

For the people- Most of this data is presented via channels (either social media, news, etc.) for public benefit be it travel tips, daily news feeds, product price comparisons, etc.

Essentially, data democratization has come to mean that by leveraging cloud computing, data that’s mostly user-generated on the internet has become accessible by all industries- big or small for their own internal use (commercial or not). This democratization has been put to use for unearthing hidden patterns from big blobs of datasets. Use cases have evolved with the consumer internet landscape and Big Data is now being used for various other means quite unanticipated.

With respect to the democratization, we’ve also heard enough about how data analytics is paving way beyond data analysts within companies and becoming available to even the non-tech-savvies. But did anyone mention DaaS providers who aid in the very first phase of data acquisition? Data scraping or web crawling (whatever your lingo is) has come to become an indivisible part of data democratization, especially when talking large-scale. The first step into bringing the public data to use is acquiring it which is where setting up web crawlers internally or partnering with DaaS providers comes to play. This blog guides towards making a choice. Its not always all the data that companies crunch or should crunch from the web. There’s obviously certain channels that are of more interest to the community than the rest and there lies the barrier- to identify sources of higher ROI and acquire data in a machine-readable format.

DaaS providers usually come to help with the entire data acquisition pipeline- starting from picking the right sources through crawl, extraction, dedup as well as data normalization based on specific requirements. Once the data has been acquired, its most likely published on another channel. Such network effect bolsters the democracy.

Steps in Data Acquisition Pipeline

crawl-extract-norm

Note- PromptCloud only delivers structured data as per the schema provided.

So while democratization may refer to easy access of computing resources in order to draw patterns from Big Data, it could also be analogous to ensuring right data in the right format at right intervals. In fact, DaaS providers have themselves used this democracy to empower it further.

Source:https://www.promptcloud.com/blog/big-data-democratization-using-web-scraping-2/

Why Businesses Need Data Scraping Service?

With the ever-increasing popularity of internet technology there is an abundance of knowledge processing information that can be used as gold if used in a structured format. We all know the importance of information. It has indeed become a valuable commodity and most sought after product for businesses. With widespread competition in businesses there is always a need to strive for better performances.

Taking this into consideration web data scraping service has become an inevitable component of businesses as it is highly useful in getting relevant information which is accurate. In the initial periods data scraping process included copying and pasting data information which was not relevant because it required intensive labor and was very costly. But now with the help of new data scraping tools like Mozenda, it is possible to extract data from websites easily. You can also take the help of data scrapers and data mining experts that scrape the data and automatically keep record of it.

How Professional Data Scraping Companies and Data Mining Experts Device a Solution?

Data Scraping Plan and Solutions

ImageCredit:http://www.loginworks.com/images/newscapingpage/data-as-service-plan.png

Why Data Scraping is Highly Essential for Businesses?

Data scraping is highly essential for every industry especially Hospitality, eCommerce, Research and Development, Healthcare, Financial and data scraping can be useful in marketing industry, real estate industry by scraping properties, agents, sites etc., travel and tourism industry etc. The reason for that is it is one of those industries where there is cut-throat competition and with the help of data scraping tools it is possible to extract useful information pertaining to preferences of customers, their preferred location, strategies of your competitors etc.

It is very important in today’s dynamic business world to understand the requirements of your customers and their preferences. This is because customers are the king of the market they determine the demand. Web data scraping process will help you in getting this vital information. It will help you in making crucial decisions which are highly critical for the success of business. With the help of data scraping tools you can automate the data scraping process which can result in increased productivity and accuracy.

Reasons Why Businesses Opt. For Website Data Scraping Solutions:

Website Scraping
Demand For New Data:

There is an overflowing demand for new data for businesses across the globe. This is due to increase in competition. The more information you have about your products, competitors, market etc. the better are your chances of expanding and persisting in competitive business environment. The manner in which data extraction process is followed is also very important; as mere data collection is useless. Today there is a need for a process through which you can utilize the information for the betterment of the business. This is where data scraping process and data scraping tools come into picture.

ImageCredit:3idatascraping.com
Capitalize On Hot Updates:

Today simple data collection is not enough to sustain in the business world. There is a need for getting up to date information. There are times when you will have the information pertaining to the trends in the market for your business but they would not be updated. During such times you will lose out on critical information. Hence; today in businesses it is a must to have recent information at your disposal.

The more recent update you have pertaining to the services of your business the better it is for your growth and sustenance. We are already seeing lot of innovation happening in the field of businesses hence; it is very important to be on your toes and collect relevant information with the help of data scrapers. With the help of data scrapping tools you can stay abreast with the latest developments in your business albeit; by spending extra money but it is necessary tradeoff in order to grow in your business or be left behind like a laggard.

Analyzing Future Demands:

Foreknowledge about the various major and minor issues of your industry will help you in assessing the future demand of your product / service. With the help of data scraping process; data scrapers can gather information pertaining to possibilities in business or venture you are involved in. You can also remain alert for changes, adjustments, and analysis of all aspects of your products and services.

Appraising Business:

It is very important to regularly analyze and evaluate your businesses. For that you need to evaluate whether the business goals have been met or not. It is important for businesses to know about your own performance. For example; for your businesses if the world market decides to lower the prices in order to grow their customer base you need to be prepared whether you can remain in the industry despite lowering the price. This can be done only with the help of data scraping process and data scraping tools.

Source:http://www.habiledata.com/blog/why-businesses-need-data-scraping-service

Example of Scraping with Selenium WebDriver in C#

In this article I will show you how it is easy to scrape a web site using Selenium WebDriver. I will guide you through a sample project which is written in C# and uses WebDriver in conjunction with the Chrome browser to login on the testing page and scrape the text from the private area of the website.

Downloading the WebDriver

First of all we need to get the latest version of Selenium Client & WebDriver Language Bindings and the Chrome Driver. Of course, you can download WebDriver bindings for any language (Java, C#, Python, Ruby), but within the scope of this sample project I will use the C# binding only. In the same manner, you can use any browser driver, but here I will use Chrome.

After downloading the libraries and the browser driver we need to include them in our Visual

Studio solution:

VS Solution

Creating the scraping program

In order to use the WebDriver in our program we need to add its namespaces:

using OpenQA.Selenium;
using OpenQA.Selenium.Chrome;
using OpenQA.Selenium.Support.UI;

Then, in the main function, we need to initialize the Chrome Driver:

using (var driver = new ChromeDriver())

{

This piece of code searches for the chromedriver.exe file. If this file is located in a directory different from the directory where our program is executed, then we need to specify explicitly its path in the ChromeDriver constructor.

When an instance of ChromeDriver is created, a new Chrome browser will be started. Now we can control this browser via the driver variable. Let’s navigate to the target URL first:

driver.Navigate().GoToUrl("http://testing-ground.scraping.pro/login");

Then we can find the web page elements needed for us to login in the private area of the website:

var userNameField = driver.FindElementById("usr");
var userPasswordField = driver.FindElementById("pwd");
var loginButton = driver.FindElementByXPath("//input[@value='Login']");

Here we search for user name and password fields and the login button and put them into the corresponding variables. After we have found them, we can type in the user name and the password  and press the login button:

userNameField.SendKeys("admin");
userPasswordField.SendKeys("12345");
loginButton.Click();

At this point the new page will be loaded into the browser, and after it’s done we can scrape the text we need and save it into the file:

var result = driver.FindElementByXPath("//div[@id='case_login']/h3").Text;

File.WriteAllText("result.txt", result);

That’s it! At the end, I’d like to give you a bonus – saving a screenshot of the current page into a file:

driver.GetScreenshot().SaveAsFile(@"screen.png", ImageFormat.Png);

The complete program listing

using System.IO;
using System.Text;
using OpenQA.Selenium;
using OpenQA.Selenium.Chrome;
using OpenQA.Selenium.Support.UI;

namespace WebDriverTest
{
    class Program
    {
        static void Main(string[] args)
        {
            // Initialize the Chrome Driver
            using (var driver = new ChromeDriver())
            {
                // Go to the home page
                driver.Navigate().GoToUrl("http://testing-ground.scraping.pro/login");

                // Get the page elements
                var userNameField = driver.FindElementById("usr");
                var userPasswordField = driver.FindElementById("pwd");
                var loginButton = driver.FindElementByXPath("//input[@value='Login']");

                // Type user name and password
                userNameField.SendKeys("admin");
                userPasswordField.SendKeys("12345");

                // and click the login button
                loginButton.Click();

                // Extract the text and save it into result.txt
                var result = driver.FindElementByXPath("//div[@id='case_login']/h3").Text;
                File.WriteAllText("result.txt", result);

                // Take a screenshot and save it into screen.png
                driver.GetScreenshot().SaveAsFile(@"screen.png", ImageFormat.Png);
            }
        }
    }
}

Also you can download a ready project here.

Conclusion

I hope you are impressed with how easy it is to scrape web pages using the WebDriver. You can naturally press keys and click buttons as you would in working with the browser. You don’t even need to understand what kind of HTTP requests are sent and what cookies are stored; the browser does all this for you. This makes the WebDriver a wonderful tool in the hands of a web scraping specialist.

Source:http://scraping.pro/example-of-scraping-with-selenium-webdriver-in-csharp/

Web Scraping: Business Intelligence

Web scraping is simply getting of information that is both hidden and unhidden from the internet. Web scraping is one of the latest technologies used in harvesting data from WebPages. It has been used to extract useful information for practical and beneficial applications and its interpretation has been tested in decision making. Web scraping is a new term that overshadows the traditional data harvesting technique that was used before. It has been regarded as knowledge discovery in databases for research and even marketing monitoring.

This article explores the various business intelligence ways in which web scraping can be used to be of importance.

Web scraping services has been used by many companies that have a strong customer focus. These companies range from sectors like retail, financial services, and marketing and communication organizations. It quite important to realize that web scraping has great signifies and impact in the varied commercial applications for the better understanding and prediction of the critical data. The data may range from stocks to consumer behaviors. The consumer behaviors are better shown in trends like customer profiles, purchasing and industry analysis among others.

Source:http://www.loginworks.com/blogs/web-scraping-blogs/web-scraping-business-intelligence/

Web Scraping Popularity Soars

The world is stirred because of the ever-growing web scraping success in almost all of its services. Success stories pertaining to the benefits of online data collection in business, research, politics, health, and almost all aspects of human life are endless. With this popularity surge, it has become a hot issue and many are questioning its legality and reliability.

Looking back, this simple harvesting of pertinent data from competitors and the global market in general like anything else started as a non-threatening and advanced form of web research. Eventually, when the benefits begin to manifest and the system improves, many are lured into it that it has become one of the strongest and fastest growing business in the world.

Simple Beginnings

As naturally as a law of life that great things come from small beginnings, data mining was conceived as a process in gaining information, mostly in research. This act of collecting information through the internet was never imagined to be what it has become nowadays.

Source:http://www.loginworks.com/blogs/web-scraping-blogs/web-scraping-popularity-soars/

Scraping webdata from a website that loads data in a streaming fashion

I'm trying to scrape some data off of the FEC.gov website using python for a project of mine. Normally I use python

mechanize and beautifulsoup to do the scraping.

I've been able to figure out most of the issues but can't seem to get around a problem. It seems like the data is

streamed into the table and mechanize.Browser() just stops listening.

So here's the issue: If you visit http://query.nictusa.com/cgi-bin/can_ind/2011_P80003338/1/A ... you get the first 500

contributors whose last name starts with A and have given money to candidate P80003338 ... however, if you use

browser.open() at that url all you get is the first ~5 rows.

I'm guessing its because mechanize isn't letting the page fully load before the .read() is executed. I tried putting a

time.sleep(10) between the .open() and .read() but that didn't make much difference.

And I checked, there's no javascript or AJAX in the website (or at least none are visible when you use the 'view-

source'). SO I don't think its a javascript issue.

Any thoughts or suggestions? I could use selenium or something similar but that's something that I'm trying to avoid.

-Will

2 Answers

Why not use an html parser like lxml with xpath expressions.

I tried

>>> import lxml.html as lh
>>> data = lh.parse('http://query.nictusa.com/cgi-bin/can_ind/2011_P80003338/1/A')
>>> name = data.xpath('/html/body/table[2]/tr[5]/td[1]/a/text()')
>>> name
[' AABY, TRYGVE']
>>> name = data.xpath('//table[2]/*/td[1]/a/text()')
>>> len(name)
500
>>> name[499]
' AHMED, ASHFAQ'
>>>



Similarly, you can create xpath expression of your choice to work with.


Source: http://stackoverflow.com/questions/9435512/scraping-webdata-from-a-website-that-loads-data-in-a-streaming-

fashion

How can I circumvent page view limits when scraping web data using Python?

I am using Python to scrape US postal code population data from http:/www.city-data.com, through this directory: http://www.city-data.com/zipDir.html. The specific pages I am trying to scrape are individual postal code pages with URLs like this: http://www.city-data.com/zips/01001.html. All of the individual zip code pages I need to access have this same URL Format, so my script simply does the following for postal_code in range:

    Creates URL given postal code
    Tries to get response from URL
    If (2), Check the HTTP of that URL
    If HTTP is 200, retrieves the HTML and scrapes the data into a list
    If HTTP is not 200, pass and count error (not a valid postal code/URL)
    If no response from URL because of error, pass that postal code and count error
    At end of script, print counter variables and timestamp

The problem is that I run the script and it works fine for ~500 postal codes, then suddenly stops working and returns repeated timeout errors. My suspicion is that the site's server is limiting the page views coming from my IP address, preventing me from completing the amount of scraping that I need to do (all 100,000 potential postal codes).

My question is as follows: Is there a way to confuse the site's server, for example using a proxy of some kind, so that it will not limit my page views and I can scrape all of the data I need?

Thanks for the help! Here is the code:

##POSTAL CODE POPULATION SCRAPER##

import requests

import re

import datetime

def zip_population_scrape():

    """
    This script will scrape population data for postal codes in range
    from city-data.com.
    """
    postal_code_data = [['zip','population']] #list for storing scraped data

    #Counters for keeping track:
    total_scraped = 0
    total_invalid = 0
    errors = 0


    for postal_code in range(1001,5000):

        #This if statement is necessary because the postal code can't start
        #with 0 in order for the for statement to interate successfully
        if postal_code <10000:
            postal_code_string = str(0)+str(postal_code)
        else:
            postal_code_string = str(postal_code)

        #all postal code URLs have the same format on this site
        url = 'http://www.city-data.com/zips/' + postal_code_string + '.html'

        #try to get current URL
        try:
            response = requests.get(url, timeout = 5)
            http = response.status_code

            #print current for logging purposes
            print url +" - HTTP:  " + str(http)

            #if valid webpage:
            if http == 200:

                #save html as text
                html = response.text

                #extra print statement for status updates
                print "HTML ready"

                #try to find two substrings in HTML text
                #add the substring in between them to list w/ postal code
                try:           

                    found = re.search('population in 2011:</b> (.*)<br>', html).group(1)

                    #add to # scraped counter
                    total_scraped +=1

                    postal_code_data.append([postal_code_string,found])

                    #print statement for logging
                    print postal_code_string + ": " + str(found) + ". Data scrape successful. " + str(total_scraped) + " total zips scraped."
                #if substrings not found, try searching for others
                #and doing the same as above   
                except AttributeError:
                    found = re.search('population in 2010:</b> (.*)<br>', html).group(1)

                    total_scraped +=1

                    postal_code_data.append([postal_code_string,found])
                    print postal_code_string + ": " + str(found) + ". Data scrape successful. " + str(total_scraped) + " total zips scraped."

            #if http =404, zip is not valid. Add to counter and print log        
            elif http == 404:
                total_invalid +=1

                print postal_code_string + ": Not a valid zip code. " + str(total_invalid) + " total invalid zips."

            #other http codes: add to error counter and print log
            else:
                errors +=1

                print postal_code_string + ": HTTP Code Error. " + str(errors) + " total errors."

        #if get url fails by connnection error, add to error count & pass
        except requests.exceptions.ConnectionError:
            errors +=1
            print postal_code_string + ": Connection Error. " + str(errors) + " total errors."
            pass

        #if get url fails by timeout error, add to error count & pass
        except requests.exceptions.Timeout:
            errors +=1
            print postal_code_string + ": Timeout Error. " + str(errors) + " total errors."
            pass


    #print final log/counter data, along with timestamp finished
    now= datetime.datetime.now()
    print now.strftime("%Y-%m-%d %H:%M")
    print str(total_scraped) + " total zips scraped."
    print str(total_invalid) + " total unavailable zips."
    print str(errors) + " total errors."



Source: http://stackoverflow.com/questions/25452798/how-can-i-circumvent-page-view-limits-when-scraping-web-data-using-python