USR-Lib » Web Sematics

Online Hacking: Using Your Credit Card Online

blogadmin — Tue, 11 Oct 2011 12:26:44 +0000

Online hacking of credit card information is an abomination arisen out of a phenomenon that has otherwise done only good for the betterment of mankind.

Purchasing products and services on the internet using credit cards has always been dicey for most people since the early days of the internet. While the internet has evolved, bringing new and improved security measures, the danger of online hacking still lurks. And the trouble with online hacking is that the perpetrators of online hacking are always a step ahead; they discover a fault or a security hole in a system and exploit it to gain unauthorized access to information. When the fault is discovered, they are filled up, leaving online hacking experts to look for other fault lines; and they usually do find them. When it comes to online usage of credit cards, the ramifications of identity theft, even without considering the potential financial loss, are huge. Your credit card leaves an electronic trail of purchases and anybody stealing the credit card details could effectively be using it for something illegal in your name.

The situation might seem hopeless and you may think that there’s not much you can do short of vowing never to use your credit card online, but that is drastic, unnecessary and inconvenient. Sure, some online merchant sites have other payment options too, but the credit card still remains the most widely offered option. Not using a credit card online is likely to prevent you from availing the awesome deals and sales you can participate in on the internet shopping sites. There are, however, ways and means to improve your defenses against online hacking.

Protecting your Credit Card Information from Online Hacking

The first thing you need to check, when shopping online with a credit card, is to ensure that you’re always on a secure page. Online merchant sites often use a high level of on-page encryption to render their pages secure, so that the sensitive information your provided may not be intercepted. This, however, is unlikely to protect you if you have malware such as keyloggers installed on your system, which is one way that perpetrators of online hacking gain your information. Therefore, using anti-virus as well as anti-spywate software is essential. Another trick that you may be subjected to is through phishing emails. It works like this: you get an email that seems to be from your credit-card company or bank, and everything looks like the real deal. The mail would request you to click on a link which would also look a genuine page of the bank or credit card company. This page, however, is a phishing page, one way of online hacking, that would ask you for your password, credit card number or some other sensitive detail (and you wouldn’t suspect a thing because it seems natural for the page to ask this information). This method of online hacking literally tricks you into giving away your own details. So be careful of any email claiming to be from your bank-salsh-credit card company-slash-online payment processor; doubly so if it contains a link to click on. Always access your bank site or any financial transaction site directly by typing the address into your online browser, the old fashioned way.

Virtual Credit Cards to Fool Online Hacking

Using a payment processor such as Paypal is also a good way to protect yourself while shopping with a credit card online. You’re furnishing your information to only one site (Paypal, in this case) which then proceeds to make payments to the sites you do your purchases from, thus limiting risk by limiting online exposure of your data. The downside is that while these payment processors have a good coverage of online merchant sites to provide you the service, there are still an awful lot of sites which wouldn’t be covered by your payment processors. You see, all processors don’t work for all websites or, for that matter, for all geographical locations.

Using virtual credit cards is another alternative to using credit cards online. These cards eliminate the risk involved in your online credit card transactions by giving you single time useable credit card numbers that can only be used online, and only be used once. Contact your credit card company to know more of these facilities that would help you shop online with your credit card.

Keep watching this space for cyber-security tips and tricks to protect yourself from the dangers of online hacking, whether for credit card information or otherwise.

Open Text bets are on Nstein’s Text Mining Engine

admin — Sat, 12 Jun 2010 12:04:50 +0000

Open Text (news, site) is starting to reveal its plans in regards to the recently closed deal on Nstein Technologies. Just as we thought, the first bets are on Nstein’s Text Mining Engine (TME) — now known as Open Text Content Analytics — and its integration with the entire Open Text Enterprise CMS Suite.

What’s Shaking at Nstein?

The Québécoise home will (for now) remain Nstein’s grounds with headquarters still in Montreal and focus on dev activities for content analytics and semantic technology.

While nothing is official yet as to how other departments and groups of Nstein will be impacted by the acquisition, we can say confidently there will probably be some cuts, trimming down the “fat” around the core activity – product development.

Open Text also sings a very familiar song saying that it will continue to support Nstein’s products — TME, DAM and Web CMS — although clearly the favorite child in this family is the TME.

Nstein’s Content Analytics Technology

As we have told you in the past, Nstein’s TME is a powerful beast that uses enhancedsemantic analysis to extract ‘linguistic DNA’ in order to enable searches that provide not only precise responses to search parameters, but also related content.

Nstein’s software creates a “semantic fingerprint” for an organization’s content, allowing them to unlock content’s value and make it more findable, visible, understandable, organized and analysis-ready.

Other linguistic features include:

Sentiment analysis
Metadata awareness
Language awareness
Broad taxonomical capabilities

And this is how it comes together with Enterprise CMS-type content, including documents, e-mail, social media, web content, OCR-treated scanned images and content in business systems, such as Microsoft, SAP and Oracle.

TME + ECM Integration Points

Initially, Open Text plans to integrate content analytics with Vignette Content Management (VCM) and Media Management (which is due for a refresher).

Next, will come its Enterprise Library — the content repository at the core of the ECM Suite — where content analytics will be providing additional functionalities for content retrieval on top of the existing native search.

Over the next year, the plan is to make content analytics available as part of every application that is in the Open Text ECM Suite, including the recently released Open Text Everywhere mobile ECM product.

Other integration points for content analytics are as follows:

Expand the use of Nstein’s technology in the area of user engagement
Adding content analytics to its WCM systems like (formerly known as) RedDot
Leveraging it in Open Text Social Media (also has been renamed recently to Open Text Social Workplace) (+if there’s still anyone there on the SM side left to work on the integration)
Use in compliance and litigation readiness scenarios for content archiving and retention management
Applicable in records management thanks to its classification technology
In eDiscovery, can be used for content collection and review.

So much to do! Let’s see how it works out for both Open Text and Nstein.

The Semantic Web and Libraries

admin — Thu, 04 Feb 2010 04:19:11 +0000

Few topics cause more concern and confusion in the web community than the Semantic Web. The Semantic Web has been described as a vision of a web that goes beyond billions of linked web documents that lay in wait to be indexed by global search engines, it is a web where the semantics, or meaning, behind the content can be utilized in a meaningful way. To some, this hearkens back to the failed promises of Artificial Intelligence computing and the non-delivery of systems that were supposed to work out the family’s budget and intelligently order groceries for the week. The World Wide Web Consortium’s (W3C) extensive work on the Semantic Web has also been characterized as taking place in a semantic “cloud” that has obscured and detracted from much-needed web standardization efforts.

If you look beyond the hype, the Semantic Web can, in some ways, be seen as a natural progression that comes from building more capabilities into every new web technology. A simple sequence describing the evolution of the Semantic Web might begin with the chaotic stage of early HTML documents, where a minimal set of tags described all manner of content. Along the way, it was realized that it would be helpful to have concepts like “author” described in more meaningful tags than “h1″ or “bold”. XML emerged as the solution to ensure that the syntax and content of documents were consistent and to allow applications better ways of working with groups of documents that are authored for a common purpose, such as finding aids and full text materials marked up in TEI. XML uses constructs called DTDs and Schemas to tightly control the structure of documents and was met with great enthusiasm by web developers who could now share information using tags with labels like “subject” that better reflect the content itself.

XML is arguably a key building block in the Semantic Web but the first real manifestation of the W3C’s semantic work was the publication of the Resource Description Framework (RDF) specification for encoding and sharing metadata. Metadata is sometimes called “data about data” and has been one of the main activities of libraries for several centuries. The premise of RDF is that metadata can be modeled as a set of statements that indicate a piece of information about something else. In RDF parlance, these are called “triples’. For example, the statement “Tim Severin is the creator of the Brendan Voyage” consists of three parts (Tim Severin, Creator, Brendan Voyage) and can be written with RDF in XML as:

Tim Severin

This type of statement is called an assertion and RDF specifies that every part of the assertion can be assigned a URI (Uniform Resource Indicator), much like a URL but different in the sense that it doesn’t have to map to a real web address and can represent concepts (“Creator”), living entities (“Tim Severin”), and anything else in the known and imagined universe, from animals to laundry lists. The “dc” in the example stands for Dublin Core and is associated with a special URI called a namespace(“http://purl.org/dc/elements/1.0/ “) that, in turn, is associated with a set of metadata elements. On its own, this is somewhat useful, but one of the most compelling aspects of RDF is combining elements from different metadata sets. If I had a set of elements specifying a rating system, for example, I could insert a namespace (xmlns) reference that would allow me to insert my rating as shown:

Excellent
Tim Severin

RDF detractors are quick to point out that this type of “mixing and matching” for metadata has been slow to ignite the kind of interest that has followed HTML and XML. While there is no doubt that RDF has not captured as much of the spotlight, it is worth noting that:

RDF is concerned with metadata, which isn’t always appreciated if you don’t have occasion to ponder information retrieval or if you think that keyword indexing can solve most information needs.
The syntax is somewhat convoluted, even compared to HTML and XML, and may be better represented by labeled graphs or other techniques common in Computer Science but often confusing to the novice. Tim Berners-Lee, the inventor of the World Wide Web, has proposed a much simpler syntax for RDF called Notation 3 which looks something like:

:tim :creator “The Brendan Voyage” .

In addition to the need to appreciate metadata and the syntax issues, another difficulty with the Semantic Web is that RDF is only the first step along the way. Going beyond assertions to supporting any high level of inferences, where a computer can automatically pull together concepts, really requires some understanding of RDF Schemas and Ontology Languages like DAML+OIL. RDF Schema allows concepts to be specified and related, for example, specifying that a “writer” is a type of “creator”. Ontologies are also formal representations of entities and concepts, and languages like DAML+OIL are different from RDF Schema in the sense that they provide even more options for defining relationships. For example, using Notation 3, we could have this relationship:

dc:Creator daml:equivalentTo red:PreparerName .

This would allow a program to “infer” that a real estate agreement identified with the “PreparerName” element from the Real Estate Data (red) Consortium schema is equivalent to “Creator” from Dublin Core using the “equivalentTo” property from DAML+OIL. This means that in addition to titles of monographs that the author I am researching has written, I could also receive documents that represent the author’s activities as a lawyer from a semantically-aware library system.

RDF Schemas and ontology work are crucial to the success of the Semantic Web, and have tended to emerge in subject areas that lend themselves well to defining relationships between concepts, for examples, dictionaries and vocabularies, thesauri, and many branches of science. For libraries, the value of the Semantic Web may have less to do with changes in bibliographic databases than with integrating resources that don’t often show up in traditional cataloguing. Scientific datasets, for example, often don’t have access points that translate well to bibliographic descriptions and bring in a multitude of concepts that may be critical for the resource community the datasets are produced for. DNA sequences, solar wind movements, and other types of scientific data require specialized query languages. RDF holds the promise of wiring in the metadata and schema/ontologies that address the complexity of the semantics of the data rather than trying to cram this level of description into Dublin Core or MARC.

Another intriguing use of Semantic Web activity is to tie together library functions with external systems. For example, expanding on the work of the RDF Calendar initiative to support queries like “find me all the works on XML that are due in the library before I go on vacation”. The Semantic Web could provide the plumbing to allow a system to talk to an individual’s RDF-enabled calendar system to determine the timeframe identified by the use of the term “vacation”. RDF and Semantic Web-based query languages offer a glimpse of how the semantics/vocabularies of different research communities may be combined in supporting information retrieval. It isn’t likely that the results will come close to the early promises of Artificial Intelligence but libraries are in a somewhat unique position to both appreciate the importance of sharing metadata, and understand the benefits of interoperable vocabularies and semantics better than most organizations. The Semantic Web may turn out to be far less audacious in practice than in concept, but it could be an important tool for trying to provide services for the growing stream of diverse web-based content and services that flows by our libraries.