Use of an object-based system with reasoning capabilities to integrate relational databases

The integration of heterogeneous and autonomous information sources is a requirement for the new type of cooperative information systems. In this paper we show the advantages of using a terminological system for integrating pre-existing relational databases. From the resulting integrated schema point of view, using · a terminological system allows for the definition of semantically richer integrated schema. From the integrated schema generation process point of view, the use of a terminological system permits the definition of a more consistent, broad and automatic process. Last, from the query processing point of view, terminological systems provide interesting features for incorporating semantic and caching query optimization techniques. The advantages are presented in detail for each main step of the integration process: translation, integration and query processing

OQAFMA Querying Agent for the Foundational Model of Anatomy: a Prototype for Providing Flexible and Efficient Access to Large Semantic Networks

The development of large semantic networks, such as the UMLS, which are intended to support a variety of applications, requires a exible and e cient query interface for the extraction of information. Using one of the source vocabularies of UMLS as a test bed, we have developed such a prototype query interface. We rst identify common classes of queries needed by applications that access these semantic networks. Next, we survey STRUQL, an existing query language that we adopted, which supports all of these classes of queries. We then describe the OQAFMA Querying Agent for the Foundational Model of Anatomy (OQAFMA), which provides an e cient implementation of a subset of STRUQL by pre-computing a variety of indices. We describe how OQAFMA leverages database optimization by converting STRUQL queries to SQL. We evaluate the exibility and e ciency of our implementation using English queries written by anatomists. This evaluation veri es that OQAFMA provides exible, e cient access to one such large semantic network, the Foundational Model of Anatomy, and suggests that OQAFMA could be an e cient query interface to other large biomedical knowledge bases, such as the Uni ed Medical Language System

Open issues in semantic query optimization in relational DBMS

After two decades of research into Semantic Query Optimization (SQO) there is clear agreement as to the efficacy of SQO. However, although there are some experimental implementations there are still no commercial implementations. We first present a thorough analysis of research into SQO. We identify three problems which inhibit the effective use of SQO in Relational Database Management Systems(RDBMS). We then propose solutions to these problems and describe first steps towards the implementation of an effective semantic query optimizer for relational databases

Knowledge-infused and Consistent Complex Event Processing over Real-time and Persistent Streams

Emerging applications in Internet of Things (IoT) and Cyber-Physical Systems (CPS) present novel challenges to Big Data platforms for performing online analytics. Ubiquitous sensors from IoT deployments are able to generate data streams at high velocity, that include information from a variety of domains, and accumulate to large volumes on disk. Complex Event Processing (CEP) is recognized as an important real-time computing paradigm for analyzing continuous data streams. However, existing work on CEP is largely limited to relational query processing, exposing two distinctive gaps for query specification and execution: (1) infusing the relational query model with higher level knowledge semantics, and (2) seamless query evaluation across temporal spaces that span past, present and future events. These allow accessible analytics over data streams having properties from different disciplines, and help span the velocity (real-time) and volume (persistent) dimensions. In this article, we introduce a Knowledge-infused CEP (X-CEP) framework that provides domain-aware knowledge query constructs along with temporal operators that allow end-to-end queries to span across real-time and persistent streams. We translate this query model to efficient query execution over online and offline data streams, proposing several optimizations to mitigate the overheads introduced by evaluating semantic predicates and in accessing high-volume historic data streams. The proposed X-CEP query model and execution approaches are implemented in our prototype semantic CEP engine, SCEPter. We validate our query model using domain-aware CEP queries from a real-world Smart Power Grid application, and experimentally analyze the benefits of our optimizations for executing these queries, using event streams from a campus-microgrid IoT deployment.Comment: 34 pages, 16 figures, accepted in Future Generation Computer Systems, October 27, 201