Though research on the Semantic Web has progressed at a steady pace, its promise has yet to be realized. One major difficulty is that, by its very nature, the Semantic Web is a large, uncensored system to which anyone may contribute. This raises

Many problems encountered when building applications of database systems involve the manipulation of models. By “model, ” we mean a complex structure that represents a design artifact, such as a relational schema, object-oriented interface, UML model, XML DTD, web-site schema, semantic network, complex document, or software configuration. Many uses of models involve managing changes in models and transformations of data from one model into another. These uses require an explicit representation of “mappings ” between models. We propose to make database systems easier to use for these applications by making “model ” and “model mapping ” first-class objects with special operations that simplify their use. We call this capability model management. In addition to making the case for model management, our main contribution is a sketch of a proposed data model. The data model consists of formal, object-oriented structures for representing models and model mappings, and of high-level algebraic operations on those structures, such as matching, differencing, merging, function application, selection, inversion and instantiation. We focus on structure and semantics, not implementation. 1

Despite the importance of spatial networks in real-life applications, most of the spatial database literature focuses on Euclidean spaces. In this paper we propose an architecture that integrates network and Euclidean information, capturing pragmatic constraints. Based on this architecture, we develop a Euclidean restriction and a network expansion framework that take advantage of location and connectivity to efficiently prune the search space. These frameworks are successfully applied to the most popular spatial queries, namely nearest neighbors, range search, closest pairs and edistance joins, in the context of spatial network databases.

Abstract—Efficient path computation is essential for applications such as intelligent transportation systems (ITS) and network routing. In ITS navigation systems, many path requests can be submitted over the same, typically huge, transportation network within a small time window. While path precomputation (path view) would provide an efficient path query response, it raises three problems which must be addressed: 1) precomputed paths exceed the current computer main memory capacity for large networks; 2) disk-based solutions are too inefficient to meet the stringent requirements of these target applications; and 3) path views become too costly to update for large graphs (resulting in out-of-date query results). We propose a hierarchical encoded path view (HEPV) model that addresses all three problems. By hierarchically encoding partial paths, HEPV reduces the view encoding time, updating time and storage requirements beyond previously known path precomputation techniques, while significantly minimizing path retrieval time. We prove that paths retrieved over HEPV are optimal. We present complete solutions for all phases of the HEPV approach, including graph partitioning, hierarchy generation, path view encoding and updating, and path retrieval. In this paper, we also present an in-depth experimental evaluation of HEPV based on both synthetic and real GIS networks. Our results confirm that HEPV offers advantages over alternative path finding approaches in terms of performance and space efficiency. Index Terms—Path queries, path view materialization, hierarchical path search, GIS databases, graph partitioning. 1

We present a priority system which is particularly suited for production rules coupled to databases. In this system, there are default priorities between all rules and overriding user-defined priorities between particular rules. Rule processing using this system is repeatable: for a given set of rules and priorities, the rules are considered for execution in the same order if the same set of transactions is executed twice on the same initial database state. The rule order adheres to the default order as closely as possible: rules are considered in the same order as the default order unless user-defined precedence constraints force an inversion. We present data structures and efficient algorithms for implementing such a priority system. We show how the data structures can be incrementally maintained as user-defined priorities are altered. We also discuss how the proposed scheme can be extended to build a multi-level hierarchical priority system. 1 Introduction Incorporation of producti...

Efficient path query processing is a key requirement for advanced database applications including GIS (Geographic Information Systems) and ITS (Intelligent Transportation Systems). We study the problem in the context of automobile navigation systems where a large number of path requests can be submitted over the transportation network within a short period of time. To guarantee efficient responsefor path queries, we employa path view materialization strategy for precomputing the best paths. We tackle the following three issues: (1) memory-resident solutions quickly exceed current computer storage capacity for networks of thousands of nodes, (2) diskbased solutions have been found inefficient to meet the stringent performance requirements, and (3) path views become too costly to update for large graphs. We propose the HEPV (Hierarchical Encoded Path View) approach that addresses these problems while guaranteeing the optimality of path retrieval. Our experimental results reveal that HEPV...

An important feature of database technology of the nineties is the use of parallelism for speeding up the execution of complex queries. This technology is being tested in several experimental database architectures and a few commercial systems for conventional select-project-join queries. In particular, hash-based fragmentation is used to distribute data to disks under the control of different processors in order to perform selections and joins in parallel. With the development of new query languages, and in particular with the definition of transitive closure queries and of more general logic programming queries, the new dimension of recursion has been added to query processing. Recursive queries are complex; at the same time, their regular structure is particularly suited for parallel execution, and parallelism may give a high efficiency gain. We survey the approaches to parallel execution of recursive queries that have been presented in the recent literature. We observe that research on parallel execution of recursive queries is separated into two distinct subareas, one focused on the transitive closure of Relational Algebra expressions, the other one focused on optimization of more general Datalog queries. Though the subareas seem radically different because of the approach and formalism used, they have many common features. This is not surprising, because most typical Datalog queries can be solved by means of the transitive closure of simple

Abstract. Question answering systems users may find answers without any supporting information insufficient for determining trust levels. Once those question answering systems begin to rely on source information that varies greatly in quality and depth, such as is typical in web settings, users may trust answers even less. We address this problem by augmenting answers with optional information about the sources that were used in the answer generation process. In addition, we introduce a trust infrastructure, IWTrust, which enables computations of trust values for answers from the Web. Users of IWTrust have access to sources used in answer computation along with trust values for those source, thus they are better able to judge answer trustworthiness. 1

Efficient path query processing necessary for route guidance has been identified as one of the key requirements for Intelligent Transportation Systems (ITS) applications. While precomputing the view of all shortest paths provides the most efficient path retrieval, the view maintenance and storage costs become unrealistic for large ITS networks. Based on ITS road type classification, we propose a hierarchical path view approach, in which the path view maintenance and storage costs are dramatically reduced at the cost of negligible loss of path optimality. Comparing with the traditional ITS path finding approaches that use A or hierarchical A , our hierarchical approach is superior in three areas: 1) path search is more efficient, 2) the connecting point from the low-level roads to the high-level roads and vice versa are dynamically determined based on the most recent traffic, 3) within one region, the high-level traffic can be dynamically rerouted through the low-level roads. In ...

Recent work proposed extending the OLAP data model to support data ambiguity, specifically imprecision and uncertainty. A process called allocation was proposed to transform a given imprecise fact table into a form, called the Extended Database, that can be readily used to answer OLAP aggregation queries. In this work, we present scalable, efficient algorithms for creating the Extended Database (i.e., performing allocation) for a given imprecise fact table. Many allocation policies require multiple iterations over the imprecise fact table, and the straightforward evaluation approaches introduced earlier can be highly inefficient. Optimizing iterative allocation policies for large datasets presents novel challenges, and has not been considered previously to the best of our knowledge. In addition to developing scalable allocation algorithms, we present a performance evaluation that demonstrates their efficiency and compares their performance with respect to straightfoward approaches. 1.