A framework for information integration using ontological foundations

With the increasing amount of data, ability to integrate information has always been a competitive advantage in information management. Semantic heterogeneity reconciliation is an important challenge of many information interoperability applications such as data exchange and data integration. In spite of a large amount of research in this area, the lack of theoretical foundations behind semantic heterogeneity reconciliation techniques has resulted in many ad-hoc approaches. In this thesis, I address this issue by providing ontological foundations for semantic heterogeneity reconciliation in information integration. In particular, I investigate fundamental semantic relations between properties from an ontological point of view and show how one of the basic and natural relations between properties – inferring implicit properties from existing properties – can be used to enhance information integration. These ontological foundations have been exploited in four aspects of information integration. First, I propose novel algorithms for semantic enrichment of schema mappings. Second, using correspondences between similar properties at different levels of abstraction, I propose a configurable data integration system, in which query rewriting techniques allows the tradeoff between accuracy and completeness in query answering. Third, to keep the semantics in data exchange, I propose an entity preserving data exchange approach that reflects source entities in the target independent of classification of entities. Finally, to improve the efficiency of the data exchange approach proposed in this thesis, I propose an extended model of the column-store model called sliced column store. Working prototypes of the techniques proposed in this thesis are implemented to show the feasibility of realizing these techniques. Experiments that have been performed using various datasets show the techniques proposed in this thesis outperform many existing techniques in terms of ability to handle semantic heterogeneities and performance of information exchange

A semantic and agent-based approach to support information retrieval, interoperability and multi-lateral viewpoints for heterogeneous environmental databases

PhDData stored in individual autonomous databases often needs to be combined and interrelated. For example, in the Inland Water (IW) environment monitoring domain, the spatial and temporal variation of measurements of different water quality indicators stored in different databases are of interest. Data from multiple data sources is more complex to combine when there is a lack of metadata in a computation forin and when the syntax and semantics of the stored data models are heterogeneous. The main types of information retrieval (IR) requirements are query transparency and data harmonisation for data interoperability and support for multiple user views. A combined Semantic Web based and Agent based distributed system framework has been developed to support the above IR requirements. It has been implemented using the Jena ontology and JADE agent toolkits. The semantic part supports the interoperability of autonomous data sources by merging their intensional data, using a Global-As-View or GAV approach, into a global semantic model, represented in DAML+OIL and in OWL. This is used to mediate between different local database views. The agent part provides the semantic services to import, align and parse semantic metadata instances, to support data mediation and to reason about data mappings during alignment. The framework has applied to support information retrieval, interoperability and multi-lateral viewpoints for four European environmental agency databases. An extended GAV approach has been developed and applied to handle queries that can be reformulated over multiple user views of the stored data. This allows users to retrieve data in a conceptualisation that is better suited to them rather than to have to understand the entire detailed global view conceptualisation. User viewpoints are derived from the global ontology or existing viewpoints of it. This has the advantage that it reduces the number of potential conceptualisations and their associated mappings to be more computationally manageable. Whereas an ad hoc framework based upon conventional distributed programming language and a rule framework could be used to support user views and adaptation to user views, a more formal framework has the benefit in that it can support reasoning about the consistency, equivalence, containment and conflict resolution when traversing data models. A preliminary formulation of the formal model has been undertaken and is based upon extending a Datalog type algebra with hierarchical, attribute and instance value operators. These operators can be applied to support compositional mapping and consistency checking of data views. The multiple viewpoint system was implemented as a Java-based application consisting of two sub-systems, one for viewpoint adaptation and management, the other for query processing and query result adjustment

Feature Extraction and Duplicate Detection for Text Mining: A Survey

Text mining, also known as Intelligent Text Analysis is an important research area. It is very difficult to focus on the most appropriate information due to the high dimensionality of data. Feature Extraction is one of the important techniques in data reduction to discover the most important features. Proce- ssing massive amount of data stored in a unstructured form is a challenging task. Several pre-processing methods and algo- rithms are needed to extract useful features from huge amount of data. The survey covers different text summarization, classi- fication, clustering methods to discover useful features and also discovering query facets which are multiple groups of words or phrases that explain and summarize the content covered by a query thereby reducing time taken by the user. Dealing with collection of text documents, it is also very important to filter out duplicate data. Once duplicates are deleted, it is recommended to replace the removed duplicates. Hence we also review the literature on duplicate detection and data fusion (remove and replace duplicates).The survey provides existing text mining techniques to extract relevant features, detect duplicates and to replace the duplicate data to get fine grained knowledge to the user

Complex adaptive systems based data integration : theory and applications

Data Definition Languages (DDLs) have been created and used to represent data in programming languages and in database dictionaries. This representation includes descriptions in the form of data fields and relations in the form of a hierarchy, with the common exception of relational databases where relations are flat. Network computing created an environment that enables relatively easy and inexpensive exchange of data. What followed was the creation of new DDLs claiming better support for automatic data integration. It is uncertain from the literature if any real progress has been made toward achieving an ideal state or limit condition of automatic data integration. This research asserts that difficulties in accomplishing integration are indicative of socio-cultural systems in general and are caused by some measurable attributes common in DDLs. This research’s main contributions are: (1) a theory of data integration requirements to fully support automatic data integration from autonomous heterogeneous data sources; (2) the identification of measurable related abstract attributes (Variety, Tension, and Entropy); (3) the development of tools to measure them. The research uses a multi-theoretic lens to define and articulate these attributes and their measurements. The proposed theory is founded on the Law of Requisite Variety, Information Theory, Complex Adaptive Systems (CAS) theory, Sowa’s Meaning Preservation framework and Zipf distributions of words and meanings. Using the theory, the attributes, and their measures, this research proposes a framework for objectively evaluating the suitability of any data definition language with respect to degrees of automatic data integration. This research uses thirteen data structures constructed with various DDLs from the 1960\u27s to date. No DDL examined (and therefore no DDL similar to those examined) is designed to satisfy the law of requisite variety. No DDL examined is designed to support CAS evolutionary processes that could result in fully automated integration of heterogeneous data sources. There is no significant difference in measures of Variety, Tension, and Entropy among DDLs investigated in this research. A direction to overcome the common limitations discovered in this research is suggested and tested by proposing GlossoMote, a theoretical mathematically sound description language that satisfies the data integration theory requirements. The DDL, named GlossoMote, is not merely a new syntax, it is a drastic departure from existing DDL constructs. The feasibility of the approach is demonstrated with a small scale experiment and evaluated using the proposed assessment framework and other means. The promising results require additional research to evaluate GlossoMote’s approach commercial use potential