TEMPORAL EXTENSIONS TO RDF

The Semantic Web is based on Resource Description Framework (RDF) which is widely used in practice. RDF represents information by only binary predicates. This simple representation scheme is the basis of an elaborate layers of methodologies, called Semantic Web Layer Cake. Though simple, it is very powerful for modeling data and basic knowledge. However, it is very limited in representing their temporal variation. Reification is the method proposed in RDF for modeling temporal changes in data and knowledge. Moreover, reification is cumbersome since it requires at least four more triples to represent just one temporal fact. By their very nature, RDF repositories are large in general and reification causes them to explode in size. In this paper, we review Semantic Web techniques that are proposed for representing temporal data in RDF.

Composite Ontology-Based Medical Diagnosis Decision Support System Framework

Current medical decision support systems have evolved from the automation of medical decision routines to improving the quality of health care services. Knowledge-based systems, compared to conventional data-driven techniques, are promising to support medical decision making. However, knowledge acquisition is usually a bottleneck in the process of developing such systemsOne possibility for acquiring medical knowledge, particularly tacit knowledge, is to use data or cases in both syntactic and semantic ways. Case-based Reasoning (CBR) methodology provides a practical way of problem solving with recalled knowledge memory of solved cases. To reduce the difficulty of knowledge acquisition, this paper proposes a design of the system framework that utilizes the simplified medical knowledge:disease-symptom ontology for prediagnosis, given patients symptoms and signs as input. In the first stage, simple pattern matching is used to gather candidate diseases in diagnosis. Following that, case-based reasoning is used to refine diagnostic decision. The case base is structured with ontological knowledge model. The case retrieval process is based on semantic similarity. The diagnostic system uses a composite knowledge base, and will allow automated diagnosis recommendation. The system framework also aims at facilitating semantic explanations to the solution derived

QServ: Integrating Testing and Auditing into QoS Management of Web Services

In a web service environment, service requesters are able to locate functionally equivalent services dynamically making quality of service (QoS) the differentiating factor amongst the web services. Service providers need to formulate QoS aware services in order to remain competitive and to achieve the highest possible profit from their offerings. There are several quality attributes to consider in any operating environment and we’ve grouped these requirements into 5 major categories: Service Dependability, Architectural Flexibility, Operational Capability, Risk Exposure and Financial Accountability. In the web services environment the realization of the attributes in these quality categories has increased in complexity due to the distributed and dynamic nature of the environment. While much of the research, standards and specifications address these issues, to the knowledge of the authors, an end to end solution for managing the quality attributes in a web service environment that include both testing and auditing has not been proposed. This paper will describe some of the current research that has been conducted to address the various aspects of quality as well as introduce the design for an end-to-end solution that will include testing and auditing

A. Survey of Entity Resolution and Record Linkage Methodologies

A great deal of research is focused on formation of a data warehouse. This is an important area of research as it could save many computation cycles and thus allow accurate information provided to the right people at the right time. Two considerations when forming a data warehouse are data cleansing (including entity resolution) and with schema integration (including record linkage). Uncleansed and fragmented data requires time to decipher and may lead to increased costs for an organization, so data cleansing and schema integration can save a great many (human) computation cycles and can lead to higher organizational efficiency. In this study we survey the literature for the methodologies proposed or developed for entity resolution and record linkage. This survey provides a foundation for solving many problems in data warehousing. For instance, little or no research has been directed at the problem of maintenance of cleansed and linked relations

Design and evaluation of a Query processing system for data base nachine networks.

Ph.D. - Doctoral Progra

Comparison of historical relational query languages

We introduce a historical relational data model in which N1NF relations are used and 1-level of nesting is allowed. Attributes can either be atomic or temporal atom. An atomic attribute represents a time invariant attribute. A temporal atom consists of two components, a value and a temporal set, which is a set of times denoting the validity period of the value. We define a relational tuple calculus for this model. We follow a comparative approach towards completeness of historical query languages

BtSQL: nested bitemporal relational database query language

A nested bitemporal relational data model and its query language are implemented. The bitemporal atom (BTA) is the fundamental construct to represent temporal data and it contains 5 components: a value, the lower and upper bounds of valid time, and the lower and upper bounds of the recoding time. We consider 2 types of data structures for storing BTAs: 1) string representation and 2) abstract data-type representation. We also develop a preprocessor for translating a bitemporal structured query language (BtSQL) statement into standard SQL statements. The BtSQL includes the select, insert, delete, and update statements of the SQL, extended for bitemporal relational databases. It supports bitemporal, historical, and current context. Bitemporal context is for auditing purposes, historical context is for querying past states of a bitemporal database, and current context is for querying the snapshot state of a bitemporal database. We also evaluate the performance of the 2 alternative implementation methods by considering retrieval, insertion, and update queries