CrossNet

Web 2.0 Overview and Issues

With the proliferation of Web 2.0 applications making inroads into the Enterprises and Businesses, we are faced with the challenge of exploding digital data growth. There are a variety of contributing factors to this phenomenon, such as the commoditisation of inexpensive storage hardware, enterprise data having approached critical mass over time, and standards allowing the easy provision and exchange of information. From an enterprise standpoint, all of this information has been getting difficult to comprehend. Having just raw numerical or alphanumerical values associated with specific measured events collected into tables (databases or even, data warehouses) is no longer good enough.

Three Decades of Data Integration

While the goal is always to provide a homogeneous, unified view on data from different sources, the integration task depends on overcoming the problems of

1. Data formats – seamlessly integrates different types of data (structured, unstructured, multimedia, asynchronous)
2. Intended use of the integrated system – intelligent applications telling what you do not know and what you should know
3. Availability of resources – time, money, manpower and know-how of the state-of-the-art technologies still residing “in-the-mill”

Knowle – Semantic Web Engine for Web 3.0

Enterprises are increasingly looking into solutions that can derive new and timely business insights with (making sense of / making cent$ out of) Big Data through the convergence of data integration technologies and advanced Business Analytics. Knowle Engine makes use of a variety of data mining techniques, and semantic web technologies and enablers (ontologies) to reduce resources and time spent on the largely manual, both critical and non-critical functions in a Data Value Chain such as, data search, aggregation, cleansing, integration and analysis. Some of the main features of Knowle Engine are:

1. Data Independence and Awareness – both human-customisable and automated identification of new data sources regardless of data formats and human languages
2. Auto-Integration – guided data integration using ontologies in a conceptual layer above existing (legacy) database systems / sources (both structured and unstructured data) to minimise disruptions to business operations
3. Ontology Learner – one of the core differentiators in the Semantic Web community. It overcomes the constraint faced in terms of the lack of domain expertise during requirements gathering and project scoping phases. The Engine learns the concept of the subjects through training to generate a “first cut” ontology. Thereafter, the domain experts can improve and edit the ontology to suit enterprise needs
4. Data Visualisation – the main goal of this feature is to communicate and abstract information into a schematic form effectively through graphical means for both querying and statistical modelling
5. Intelligent Query and Analytics – description logic reasoning algorithms for uncovering previously unknown patterns and answers to support decisions