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/

Local ScraperWiki Library

It quite annoyed me that you can only use the scraperwiki library on a ScraperWiki instance; most of it could work fine elsewhere. So I’ve pulled it out (well, for Python at least) so you can use it offline.

How to use
pip install scraperwiki_local
A dump truck dumping its payload

You can then import scraperwiki in scripts run on your local computer. The scraperwiki.sqlite component is powered by DumpTruck, which you can optionally install independently of scraperwiki_local.

pip install dumptruck
Differences

DumpTruck works a bit differently from (and better than) the hosted ScraperWiki library, but the change shouldn’t break much existing code. To give you an idea of the ways they differ, here are two examples:

Complex cell values
What happens if you do this?
import scraperwiki
shopping_list = ['carrots', 'orange juice', 'chainsaw']
scraperwiki.sqlite.save([], {'shopping_list': shopping_list})
On a ScraperWiki server, shopping_list is converted to its unicode representation, which looks like this:
[u'carrots', u'orange juice', u'chainsaw']
In the local version, it is encoded to JSON, so it looks like this:
["carrots","orange juice","chainsaw"]


And if it can’t be encoded to JSON, you get an error. And when you retrieve it, it comes back as a list rather than as a string.

Case-insensitive column names
SQL is less sensitive to case than Python. The following code works fine in both versions of the library.

In [1]: shopping_list = ['carrots', 'orange juice', 'chainsaw']
In [2]: scraperwiki.sqlite.save([], {'shopping_list': shopping_list})
In [3]: scraperwiki.sqlite.save([], {'sHOpPiNg_liST': shopping_list})
In [4]: scraperwiki.sqlite.select('* from swdata')

Out[4]: [{u'shopping_list': [u'carrots', u'orange juice', u'chainsaw']}, {u'shopping_list': [u'carrots', u'orange juice', u'chainsaw']}]

Note that the key in the returned data is ‘shopping_list’ and not ‘sHOpPiNg_liST’; the database uses the first one that was sent. Now let’s retrieve the individual cell values.

In [5]: data = scraperwiki.sqlite.select('* from swdata')
In [6]: print([row['shopping_list'] for row in data])
Out[6]: [[u'carrots', u'orange juice', u'chainsaw'], [u'carrots', u'orange juice', u'chainsaw']]

The code above works in both versions of the library, but the code below only works in the local version; it raises a KeyError on the hosted version.

In [7]: print(data[0]['Shopping_List'])
Out[7]: [u'carrots', u'orange juice', u'chainsaw']

Here’s why. In the hosted version, scraperwiki.sqlite.select returns a list of ordinary dictionaries. In the local version, scraperwiki.sqlite.select returns a list of special dictionaries that have case-insensitive keys.

Develop locally

Here’s a start at developing ScraperWiki scripts locally, with whatever coding environment you are used to. For a lot of things, the local library will do the same thing as the hosted. For another lot of things, there will be differences and the differences won’t matter.

If you want to develop locally (just Python for now), you can use the local library and then move your script to a ScraperWiki script when you’ve finished developing it (perhaps using Thom Neale’s ScraperWiki scraper). Or you could just run it somewhere else, like your own computer or web server. Enjoy!

Source:https://blog.scraperwiki.com/2012/06/local-scraperwiki-library/

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/

Multiple Listing Service Gets Favorable Appellate Ruling in Scraping Lawsuit

This is a follow-up to our massive post on anti-scraping lawsuits in the real estate industry from New Year’s Eve 2012 (Note: the portion on MRIS is about halfway through the post, labeled “Same Writ, Different Plaintiff”).

AHRN is a California real estate broker that owns and operates NeighborCity.com. The site gets its data in part by scraping from MLS databases–in this case, MRIS. As part of the scraping, however, AHRN had collected and displayed copyrighted photographs among the bits and pieces of general textual information about the properties. MRIS sent a cease and desist letter to AHRN, and filed suit alleging various copyright claims after the parties failed to agree on a license to use the photographs. Ultimately, a district court in Maryland granted a motion made by MRIS for a preliminary injunction.

When we last left off, the district court had revised its preliminary injunction order to enjoin only AHRN’s use of MRIS’s photographs–not the compilation itself or any textual elements that may be considered a part of it. Since then, AHRN appealed the injunction. On July 18th, the Fourth Circuit Court of Appeals affirmed.

Background

shutterstock_108008486.jpgAHRN argued that MRIS failed to show a likelihood of success on its copyright infringement claim because MRIS: (1) failed to register its copyright in the individual photographs when it registered the database, and (2) did not have a copyright interest in the photographs because the subscribers’ electronic agreement to MRIS’s terms of use failed to transfer those rights.

 MRIS Did Not Fail to Register Its Interest in the Photographs

This first question revolved around the scope of MRIS’s registrations. AHRN argued that MRIS’s collective work registrations did not cover the individual photographs because MRIS did not identify the names of the authors and titles of those works. MRIS argued that 17 U.S.C. §409 did not require any such identification when applied to collective works, and that its general description of the pre-existing photographs’ inclusion sufficed.

The court began its discussion by noting the “ambiguous” nature of §409’s language and its varying judicial interpretations. Some courts have barred infringement suits because the collective work registrant failed to list the authors, while others have allowed infringement suits where the registrant owns the rights to the component works as well as the collective work.

In this case, the court agreed with MRIS and found that the latter approach was more consistent with the relevant statutes and regulations:

    Adding impediments to automated database authors’ attempts to register their own component works conflicts with the general purpose of Section 409 to encourage prompt registration . . . and thwarts the specific goal embodied in Section 408 of easing the burden on group registrations[.]

As part of its decision, the court looked favorably upon the 3Taps case, in which Craigslist sued 3Taps and Padmapper for scraping and repackaging its online classified ads. In that case, the court reasoned that it would be “inefficient” to require registrants to list each author of an extremely large number of component works to which the registrant already had obtained an exclusive license.

Having found that MRIS’s general description satisfied § 409’s pre-suit registration requirement, the court moved on to the merits of MRIS’s infringement claim–more specifically, the question of whether MRIS’s Terms of Use actually transferred a copyright interest to its subscribers’ photographs.

E-SIGN Applies to Assignments of Copyrights and Overrides § 204

AHRN challenged MRIS’s ownership of the photographs by arguing that an MLS subscriber’s electronic agreement to MRIS’s Terms of Use does not operate as an assignment of rights under § 204, which requires a signed “writing.”

In a bad sign for AHRN, the court began its discussion by volunteering an argument that MRIS did not even bring up:

    [I]n situations where “the copyright [author] appears to have no dispute with its [assignee] on this matter, it would be anomalous to permit a third party infringer to invoke [Section 204(a)’s signed writing requirement] against the [assignee].”

With that in mind, the court went on to discuss the E-SIGN act’s impact on the conveyance of copyrights. After establishing the meaning of “e-signature,” the court focused on whether the act was limited from covering this type of situation.

    The Act provides that it “does not . . . limit, alter, or otherwise affect any requirement imposed by a statute, regulation, or rule of law . . . other than a requirement that contracts or other records be written, signed, or in nonelectric form[.]”

The court emphasized the phrase “other than,” reasoning that a plain reading of the E-SIGN language showed that Congress intended the provisions to limit § 204. It also noted that Congress did not list copyright assignments among the various agreements to which E-SIGN did not apply–nor was there a catchall that included such assignments.

The court then turned to the Hermosilla case, in which a district court in Florida upheld the validity of a copyright conveyance via e-mail. It emphasized the Hermosilla court’s reliance on the purpose of § 204–“to resolve disputes between copyright owners and transferees and to protect copyright holders from persons mistakenly or fraudulently claiming oral licenses or copyright ownership.” The appellate court agreed with the Hermosilla court that allowing assignment via e-mail actually helped cut down on these types of disputes.

    To invalidate copyright transfer agreements solely because they were made electronically would thwart the clear congressional intent embodied in the E-Sign Act.

All in all, the court basically said “we don’t see why E-SIGN shouldn’t apply.” Note that it did not pass judgment specifically on whether MRIS’s Terms of Use constituted a valid contract. It simply mentioned that AHRN waived that argument by not bringing it up sooner.

Source: http://blog.ericgoldman.org/archives/2013/07/multiple_listin_1.htm

The Roots of Web Scraping and the Wisdom behind It

You may be wondering how data mining came into existence. This effective and innovative trend in business and research is indeed something commendable and the genius behind it is worth great reward. To have a clear view of the origin of web scraping, the following important factors that contribute to the creation of this phenomenon called data collection or web scraping are considered.

Foundations

Unlike any other innovation, no specific date can be clearly pointed out as the birthdate of data mining. It has come into existence as a result of several problem solving processes in major data gathering and handling situations. It appears that cyber technology has opened a Pandora box of “anything can happen” experiences. Moreover, the shift from physical to virtual data collection has resulted in a bulk of database that needed to be organized, analyzed and utilized.

Source: http://www.loginworks.com/blogs/web-scraping-blogs/roots-web-scraping-wisdom-behind/