An intelligent query processing for distributed ontologies

In this paper, we propose an intelligent distributed query processing method considering the characteristics of a distributed ontology environment. We suggest more general models of the distributed ontology query and the semantic mapping among distributed ontologies compared with the previous works. Our approach rewrites a distributed ontology query into multiple distributed ontology queries using the semantic mapping, and we can obtain the integrated answer through the execution of these queries. Furthermore, we propose a distributed ontology query processing algorithm with several query optimization techniques: pruning rules to remove unnecessary queries, a cost model considering site load balancing and caching, and a heuristic strategy for scheduling plans to be executed at a local site. Finally, experimental results show that our optimization techniques are effective to reduce the response time.     

Efficient co-processor utilization in database query processing

Specialized processing units such as GPUs or FPGAs provide great opportunities to speed up database operations by exploiting parallelism and relieving the CPU. However, distributing a workload on suitable (co-)processors is a challenging task, because of the heterogeneous nature of a hybrid processor/co-processor system. In this paper, we present a framework that automatically learns and adapts execution models for arbitrary algorithms on any (co-)processor. Our physical optimizer uses the execution models to distribute a workload of database operators on available (co-)processing devices. We demonstrate its applicability for two common use cases in modern database systems. Additionally, we contribute an overview of GPU-co-processing approaches, an in-depth discussion of our framework's operator model, the required steps for deploying our framework in practice and the support of complex operators requiring multi-dimensional learning strategies.     

Secure query processing against encrypted XML data using Query-Aware Decryption

Dissemination of XML data on the internet could breach the privacy of data providers unless access to the disseminated XML data is carefully controlled. Recently, the methods using encryption have been proposed for such access control. However, in these methods, the performance of processing queries has not been addressed. A query processor cannot identify the contents of encrypted XML data unless the data are decrypted. This limitation incurs overhead of decrypting the parts of the XML data that would not contribute to the query result. In this paper, we propose the notion of Query-Aware Decryption for efficient processing of queries against encrypted XML data. Query-Aware Decryption allows us to decrypt only those parts that would contribute to the query result. For this purpose, we disseminate an encrypted XML index along with the encrypted XML data. This index, when decrypted, informs us where the query results are located in the encrypted XML data, thus preventing unnecessary decryption for other parts of the data. Since the size of this index is much smaller than that of the encrypted XML data, the cost of decrypting this index is negligible compared with that for unnecessary decryption of the data itself. The experimental results show that our method improves the performance of query processing by up to six times compared with those of existing methods. Finally, we formally prove that dissemination of the encrypted XML index does not compromise security.     

An adaptable distributed query processing architecture

Traditionally, distributed query optimization techniques generate static query plans at compile time. However, the optimality of these plans depends on many parameters (such as the selectivities of operations, the transmission speeds and workloads of servers) that are not only difficult to estimate but are also often unpredictable and fluctuant at runtime. As the query processor cannot dynamically adjust the plans at runtime, the system performance is often less than satisfactory. In this paper, we introduce a new highly adaptive distributed query processing architecture. Our architecture can quickly detect fluctuations in selectivities of operations, as well as transmission speeds and workloads of servers, and accordingly change the operation order of a distributed query plan during execution. We have implemented a prototype based on the Telegraph system [Telegragraph project. Available from >]. Our experimental study shows that our mechanism can adapt itself to the changes in the environment and hence approach to an optimal plan during execution.     

A personalized query expansion approach for engineering document retrieval

Engineers create engineering documents with their own terminologies, and want to search existing engineering documents quickly and accurately during a product development process. Keyword-based search methods have been widely used due to their ease of use, but their search accuracy has been often problematic because of the semantic ambiguity of terminologies in engineering documents and queries. The semantic ambiguity can be alleviated by using a domain ontology. Also, if queries are expanded to incorporate the engineer’s personalized information needs, the accuracy of the search result would be improved. Therefore, we propose a framework to search engineering documents with less semantic ambiguity and more focus on each engineer’s personalized information needs. The framework includes four processes: (1) developing a domain ontology, (2) indexing engineering documents, (3) learning user profiles, and (4) performing personalized query expansion and retrieval. A domain ontology is developed based on product structure information and engineering documents. Using the domain ontology, terminologies in documents are disambiguated and indexed. Also, a user profile is generated from the domain ontology. By user profile learning, user’s interests are captured from the relevant documents. During a personalized query expansion process, the learned user profile is used to reflect user’s interests. Simultaneously, user’s searching intent, which is implicitly inferred from the user’s task context, is also considered. To retrieve relevant documents, an expanded query in which both user’s interests and intents are reflected is then matched against the document collection. The experimental results show that the proposed approach can substantially outperform both the keyword-based approach and the existing query expansion method in retrieving engineering documents. Reflecting a user’s information needs precisely has been identified to be the most important factor underlying this notable improvement.     

Self-monitoring query execution for adaptive query processing

Adaptive query processing generally involves a feedback loop comprising monitoring, assessment and response. So far, individual proposals have tended to group together an approach to monitoring, a means of assessment, and a form of response. However, there are many benefits in decoupling these three phases, and in constructing generic frameworks for each of them. To this end, this paper discusses monitoring of query plan execution as a topic in its own right, and advocates an approach based on self-monitoring algebraic operators. This approach is shown to be generic and independent of any specific adaptation mechanism, easily implementable and portable, sufficiently comprehensive, appropriate for heterogeneous distributed environments, and more importantly, capable of driving on-the-fly adaptations of query plan execution. An experimental evaluation of the overheads and of the quality of the results obtained by monitoring is also presented.     

Integrated approach to knowledge-based process-plan generation

An integrated approach is presented to process-plan generation with an expert system. First, the research takes full advantage of the programming language(s) embedded in existing CAD software to develop a CAM database system that is building-oriented rather than drawing-oriented, and from which drawings are produced as reports. Then, a knowledge-base system is designed to automate the development of the process plan. The application of the expert system to a mechanical part is illustrated.     

Efficient recursive XML query processing using relational database systems

Recursive queries are quite important in the context of XML databases. In addition, several recent papers have investigated a relational approach to store XML data and there is growing evidence that schema-conscious approaches are a better option than schema-oblivious techniques as far as query performance is concerned. However, the issue of recursive XML queries for such approaches has not been dealt with satisfactorily. In this paper we argue that it is possible to design a schema-oblivious approach that outperforms schema-conscious approaches for certain types of recursive queries. To that end, we propose a novel schema-oblivious approach, called Sucxent++ (Schema Unconcious XML Enabled System), that outperforms existing schema-oblivious approaches such as XParent by up to 15 times and schema-conscious approaches (Shared-Inlining) by up to eight times for recursive query execution. Our approach has up to two times smaller storage requirements compared to existing schema-oblivious approaches and 10% less than schema-conscious techniques. In addition Sucxent++ performs marginally better than Shared-Inlining and is 5.7–47 times faster than XParent as far as insertion time is concerned.     

A SAT approach to query optimization in mediator systems

Mediator systems integrate distributed, heterogeneous and autonomous data sources, but their effective use requires the solution of hard query optimization problems. This is usually done in two phases: the selection of a set of data sources is similar to a set covering problem, and their ordering into a feasible and efficient query is a capability restricted join order problem. However, a two-phase approach is unlikely to find optimum queries. We describe a new single-phase approach that, under a simple cost model, can be encoded and solved as a SAT problem. Results on artificial benchmarks indicate that this is an interesting problem from the encoding and search viewpoints, and we use them to address three of the ten SAT challenges posed by Selman, Kautz and McAllester in 1997.This research was partially funded by grant R-252-000-064-107 from the National University of Singapore, and grant 00/PI.1/C075 from Science Foundation Ireland.     

A SAT approach to query optimization in mediator systems

Mediator systems integrate distributed, heterogeneous and autonomous data sources, but their effective use requires the solution of hard query optimization problems. This is usually done in two phases: the selection of a set of data sources is similar to a set covering problem, and their ordering into a feasible and efficient query is a capability restricted join order problem. However, a two-phase approach is unlikely to find optimum queries. We describe a new single-phase approach that, under a simple cost model, can be encoded and solved as a SAT problem. Results on artificial benchmarks indicate that this is an interesting problem from the encoding and search viewpoints, and we use them to address three of the ten SAT challenges posed by Selman, Kautz and McAllester in 1997.     

A knowledge-based approach to the local area network design problem

Computer networks are an essential tool for people in business, industries, government, and schools. With the rapid rate of change in network technology and products, and the emergence of highly sophisticated network users, network design has become an increasingly complex task. Although the computer society aims at agreeing to a series of international standards for describing network architectures, the design of a computer network remains an ill-structured problem that lends itself perfectly to expert systems solutions. We propose an expert system that is able to design local area networks meeting the requirements specified by the user. Rules and guidelines pertaining to local area network design are formulated and incorporated into the knowledge base. The system is built on an object-oriented paradigm. The object-oriented approach and the hierarchical rule structure paradigm are discussed. We also employ the blackboard technique through which rules can access dynamic objects conveniently.     

An XML query rewriting mechanism with multiple ontologies integration based on complex semantic mapping

Semantic integration, which can be divided into three parts including ontology mapping, mapping representation, and reasoning and query rewriting with mappings, plays a key role in information integration systems. This paper develops an XML query rewriting and ontology integration mechanism, which acts as a global-as-view (GAV) approach to represent and query semantic information in mediator based information integration environment. It proposes the patterns and properties of ontology mappings, discusses the procedure and algorithm of ontology integration firstly, and then proposes the ontology based XML query mechanism, especially the XML query rewriting mechanism. Finally, a mediator-based implementation of the mechanism in OBSA system is introduced.     

A knowledge-based approach to maintenance project planning

Maintenance issues comprise an important, though frequently overlooked, area of manufacturing operations. In smaller companies in particular, expertise for effective maintenance project planning may be lacking. In this paper we discuss the development of an object-oriented expert system to support such project planning. The system was developed to meet the needs of an automotive components manufacturer, and focuses solely on its material-handling-oriented projects. The microcomputer-based expert system considers issues of schedule, inventory availability and cost.     

Efficiently supporting order in XML query processing

XML is an ordered data model and XQuery expressions return results that have a well-defined order. However, little work on how order is supported in XML query processing has been done to date. In this paper we study the issues related to handling order in the XML context, namely challenges imposed by the XML data model, the variety of order requirements of the XQuery language, and the need to maintain order in the presence of updates to the XML data. We propose an efficient solution that addresses all these issues. Our solution is based on a key encoding for XML nodes that serves as node identity and at the same time encodes order. We design rules for encoding order of processed XML nodes based on the XML algebraic query execution model and the node key encoding. These rules do not require any actual sorting for intermediate results during execution. Our approach enables efficient order-sensitive incremental view maintenance as it makes most XML algebra operators distributive with respect to bag union. We prove the correctness of our order encoding approach. Our approach is implemented and integrated with Rainbow, an XML data management system developed at WPI. We have tested the efficiency of our approach using queries that have different order requirements. We have also measured the relative cost of different components related to our order solution in different types of queries. In general the overhead of maintaining order in our approach is very small relative to the query processing time.     

From keywords to semantic queries—Incremental query construction on the semantic web

Constructing semantic queries is a demanding task for human users, as it requires mastering a query language as well as the schema which has been used for storing the data. In this paper, we describe QUICK, a novel system for helping users to construct semantic queries in a given domain. QUICK combines the convenience of keyword search with the expressivity of semantic queries. Users start with a keyword query and then are guided through a process of incremental refinement steps to specify the query intention. We describe the overall design of QUICK, present the core algorithms to enable efficient query construction, and finally demonstrate the effectiveness of our system through an experimental study.     

Parallel processing in knowledge-based diagnostic systems

This paper highlights the use of the parallel processing concept in knowledge-based diagnostic systems. A MIMD machine connected in a cubic mesh fashion using Parlog has been suggested to implement such systems. An algorithm supporting the concurrent execution of multiple conflict set rules of the same production system program is presented. A specific application to communication systems maintenance utilizing these principles has been discussed.     

A knowledge-based approach to design for manufacturability

In the light of growing global competition, organizations around the world today are constantly under pressure to produce high-quality products at an economical price. The integration of design and manufacturing activities into one common engineering effort has been recognized as a key strategy for survival and growth. Design for manufacturability (DFM) is an approach to design that fosters the simultaneous involvement of product design and process design. The implementation of the DFM approach requires the collaboration of both the design and manufacturing functions within an organization. Many reasons can be cited for the inability to implement the DFM approach effectively, including: lack of interdisciplinary expertise of designers; inflexibility in organizational structure, which hinders interaction between design and manufacturing functions; lack of manufacturing cost information at the design phase; and absence of integrated engineering effort intended to maximize functional and manufacturability objectives. The purpose of this research is to show how expert systems methodology could be used to provide manufacturability expertise during the design phase of a product. An object- and rule-based expert system has been developed that has the capability: (1) to make process selection decisions based on a set of design and production parameters to achieve cost-effective manufacture; and (2) to estimate manufacturing cost based on the identified processes. The expertise for primary process selection is developed for casting and forging processes. The specialized processes considered are die casting, investment casting, sand casting, precision forging, open die forging and conventional die forging. The processes considered for secondary process selection are end milling and drilling. The cost estimation expertise is developed for the die casting process, the milling and drilling operations, and the manual assembly operations. The results obtained from the application of the expert system suggest that the use of expert systems methodology is a feasible method for implementing the DFM approach.     

Supporting web query expansion efficiently using multi-granularity indexing and query processing

The problem of word mismatch in information retrieval (IR) occurs because users often use different words to describe concepts in their queries than authors use to describe the same concepts in their documents. Query expansion is used to deal with the mismatch between author and user vocabularies. To support query expansion, indices on words related by lexical semantics and syntactical co-occurrence need to be maintained. Two issues become paramount in supporting query expansion: the size of index tables and the query processing overhead. In this paper, we propose to use the notion of multi-granularity for more efficient indexing and query processing while the same degrees of precision and recall are maintained. We also describes extensions of this technique to handle: (1) query relaxation to handle words with multiple senses and with other semantic relationships; (2) progressive processing of queries with top N results and (3) progressive processing of queries with specification of the importance of each keyword.