Quality of Service for Information Access

Information is available in many forms from different sources, in distributed locations; access to information is supported by networks of varying performance; the cost of accessing and transporting the information varies for both the source and the transport route. Users who vary in their preferences, background knowledge required to interpret the information and motivation for accessing it, gather information to perform many different tasks. This position paper outlines some of these variations in information provision and access, and explores the impact these variations have on the user’s task performance, and the possibilities they make available to adapt the user interface for the presentation of information

Benchmarking Bottom-Up and Top-Down Strategies to Sparql-To-Sql Query Translation

Many researchers have proposed using conventional relational databases to store and query large Semantic Web datasets. The most complex component of this approach is SPARQL-to-SQL query translation. Existing algorithms perform this translation using either bottom-up or top-down strategy and result in semantically equivalent but syntactically different relational queries. Do relational query optimizers always produce identical query execution plans for semantically equivalent bottom-up and top-down queries? Which of the two strategies yields faster SQL queries? To address these questions, this work studies bottom-up and top-down translations of SPARQL queries with nested optional graph patterns. This work presents: (1) A basic graph pattern translation algorithm that yields flat SQL queries, (2) A bottom-up nested optional graph pattern translation algorithm, (3) A top-down nested optional graph pattern translation algorithm, and (4) A performance study featuring SPARQL queries with nested optional graph patterns over RDF databases created in Oracle, DB2, and PostgreSQL

Teaching an RDBMS about ontological constraints

International audienceIn the presence of an ontology, query answers must reflect not only data explicitly present in the database, but also implicit data, which holds due to the ontology, even though it is not present in the database. A large and useful set of ontology languages enjoys FOL reducibility of query answering: answering a query can be reduced to evaluating a certain first-order logic (FOL) formula (obtained from the query and ontology) against only the explicit facts. We present a novel query optimization framework for ontology-based data access settings enjoying FOL reducibility. Our framework is based on searching within a set of alternative equivalent FOL queries, i.e., FOL reformulations, one with minimal evaluation cost when evaluated through a relational database system. We apply this framework to the DL-LiteR Description Logic underpinning the W3C's OWL2 QL ontology language, and demonstrate through experiments its performance benefits when two leading SQL systems, one open-source and one commercial, are used for evaluating the FOL query reformulations

Keyword Join: Realizing Keyword Search for Information Integration

Information integration has been widely addressed over the last several decades. However, it is far from solved due to the complexity of resolving schema and data heterogeneities. In this paper, we propose out attempt to alleviate such difficulty by realizing keyword search functionality for integrating information from heterogeneous databases. Our solution does not require predefined global schema or any mappings between databases. Rather, it relies on an operator called keyword join to take a set of lists of partial answers from different data sources as input, and output a list of results that are joined by the tuples from input lists based on predefined similarity measures as integrated results. Our system allows source databases remain autonomous and the system to be dynamic and extensible. We have tested our system with real dataset and benchmark, which shows that our proposed method is practical and effective.Singapore-MIT Alliance (SMA

Ontology for Psychophysiological Dysregulation of Anger/Aggression

The advancement of Information Technology in the last four decades led to the use of computers in medicine. A new area called Medical Informatics has emerged. This area comprises the application of IT to healthcare with the aim of creating tools that help healthcare personnel diagnose and treat patients more accurately and efficiently. IT not only provides tools for storing, integrating, and updating patient information base but also for processing information efficiently. One of such tools is a Clinical Decision Support System. Ontologies are an integral part of clinical decision support systems because they help formalize and integrate domain knowledge. In this project, we developed a software program that assists clinicians in making diagnostic decisions about a particular problem type called ‘psychophysiological dysregulation of anger/aggression’. We created a new ontology for the problem domain. The computer program asks a set of pertinent questions and the patient or clinician on behalf of the patient is required to answer it. All these answers along with the results from various lab assessment tests are fed into the software program which then outputs a diagnosis by interacting with the ontology and also proposes the preferred treatment plan. While undergoing the treatment the patient is monitored at regular intervals by the clinician and this data is recorded as the treatment episode data. The tools and technologies used for this project are Web Ontology Language (OWL) version 2, Protégé 4.1.0 Beta, Java, Eclipse Helios IDE and IBM DB2. Adviser: Jitender S. Deogu

Ontology for Psychophysiological Dysregulation of Anger/Aggression

The advancement of Information Technology in the last four decades led to the use of computers in medicine. A new area called Medical Informatics has emerged. This area comprises the application of IT to healthcare with the aim of creating tools that help healthcare personnel diagnose and treat patients more accurately and efficiently. IT not only provides tools for storing, integrating, and updating patient information base but also for processing information efficiently. One of such tools is a Clinical Decision Support System. Ontologies are an integral part of clinical decision support systems because they help formalize and integrate domain knowledge. In this project, we developed a software program that assists clinicians in making diagnostic decisions about a particular problem type called ‘psychophysiological dysregulation of anger/aggression’. We created a new ontology for the problem domain. The computer program asks a set of pertinent questions and the patient or clinician on behalf of the patient is required to answer it. All these answers along with the results from various lab assessment tests are fed into the software program which then outputs a diagnosis by interacting with the ontology and also proposes the preferred treatment plan. While undergoing the treatment the patient is monitored at regular intervals by the clinician and this data is recorded as the treatment episode data. The tools and technologies used for this project are Web Ontology Language (OWL) version 2, Protégé 4.1.0 Beta, Java, Eclipse Helios IDE and IBM DB2. Adviser: Jitender S. Deogu