3857492
Formal Representation and Transformation of DTDs to Sem-ODM Semantic Schemas
Many projects have investigated the issue of storing XML in traditional database systems and exporting data in traditional databases as XML documents. However, they paid little attention to the formalization of the data models and transformation between them. In this paper, we address this problem by providing a formal framework in which Document Type Definition (DTD) and Semantic Schema of the Semantic Binary ObjectOriented Data Model (Sem-ODM) are formally defined and the conversion from DTD to Semantic Schema is presented. Through this formalization, we achieve preciseness and conciseness in expressing both data models and converting one to the other.
3869043
XQuery Translation to Sem-SQL
XML query translation is an inevitable step involved in using non-XML databases storing XML data. In this paper, we address the XQuery to Sem-SQL translation issue, part of the XML storage and retrieval using the Semantic Binary Object-Oriented Database System (Sem-ODB) project, by providing a high-level description of the translation scheme between XQuery and Sem-SQL. Our translation scheme utilizes the navigation-oriented paradigm of Sem-SQL in translating XQuery path expressions to avoid excessive joins. Our approach can deal with all recursive path expressions without requiring the underlying database system to support recursive queries. We also extend the sorted outer union approach with consideration on user-defined orderings.
2104066
Design and evaluation of a resource selection framework for Grid applications
While distributed, heterogeneous collections of computers ("Grids") can in principle be used as a computing platform, in practice the problems of first discovering and then organizing resources to meet application requirements are difficult. We present a general-purpose resource selection framework that addresses these problems by defining a resource selection service for locating Grid resources that match application requirements. At the heart of this framework is a simple, but powerful, declarative language based on a technique called set matching, which extends the Condor matchmaking framework to support both single-resource and multiple-resource selection. This framework also provides an open interface for loading application-specific mapping modules to personalize the resource selector. We present results obtained when this framework is applied in the context of a computational astrophysics application, Cactus. These results demonstrate the effectiveness of our technique.
3393163
Dynamic Integration Strategy for Mediation Framework
The trend in the information area is to provide integrated access to diverse and dynamic information sources. We present a modeling and architectural solution to the problem of data integration from heterogeneous data sources based on the use of wrappers and mediators. The mediator supports integrated query processing by the semantic mapping between the global schema type and the local schema types. However, mediation poses extensive security problems. Protecting proprietary data from unauthorized access is recognized as one of the most significant barriers to the mediation systems. Our approach benefits in both flexible strategy of data integration based on semantic mappings and dynamic access control in mediation systems.
2764337
Automated compile-time and run-time techniques to increase usable memory in MMU-less embedded systems
Random access memory (RAM) is tightly-constrained in many embedded systems. This is especially true for the least expensive, lowest-power embedded systems, such as sensor network nodes and portable consumer electronics. The most widely-used sensor network nodes have only 4-10 KB of RAM and do not contain memory management units (MMUs). It is very difficult to implement increasingly complex applications under such tight memory constraints. Nonetheless, price and power consumption constraints make it unlikely that increases in RAM in these systems will keep pace with the requirements of applications.We propose the use of automated compile-time and run-time techniques to increase the amount of usable memory in MMU-less embedded systems. The proposed techniques do not increase hardware cost, and are designed to require few or no changes to existing applications. We have developed a fast compression algorithm well suited to this application, as well as run-time library routines and compiler transformations to control and optimize the automatic migration of application data between compressed and uncompressed memory regions. These techniques were experimentally evaluated on Crossbow TelosB sensor network nodes running a number of data collection and signal processing applications. The results indicate that available memory can be increased by up to 50% with less than 10% performance degradation for most benchmarks.
17529460
Systematic redundant residue number system codes: analytical upper bound and iterative decoding performance over AWGN and Rayleigh channels
The novel family of redundant residue number system (RRNS) codes is studied. RRNS codes constitute maximum-minimum distance block codes, exhibiting identical distance properties to Reed-Solomon codes. Binary to RRNS symbol-mapping methods are proposed, in order to implement both systematic and nonsystematic RRNS codes. Furthermore, the upper-bound performance of systematic RRNS codes is investigated, when maximum-likelihood (ML) soft decoding is invoked. The classic Chase algorithm achieving near-ML soft decoding is introduced for the first time for RRNS codes, in order to decrease the complexity of the ML soft decoding. Furthermore, the modified Chase algorithm is employed to accept soft inputs, as well as to provide soft outputs, assisting in the turbo decoding of RRNS codes by using the soft-input/soft-output Chase algorithm
2470832
Statistical data reduction for efficient application performance monitoring
There is a growing need for systems that can monitor and analyze application performance data automatically in order to deliver reliable and sustained performance to applications. However, the continuously growing complexity of high performance computer systems and applications makes this process difficult. We introduce a statistical data reduction method that can be used to guide the selection of system metrics that are both necessary and sufficient to describe observed application behavior, thus reducing the instrumentation perturbation and data volume to be managed. To evaluate our strategy, we applied it to one CPU-bound grid application using cluster machines and GridFTP data transfer in a wide area testbed. A comparative study shows that our strategy produces better results than other techniques. It can reduce the number of system metrics to be managed by about 80%, while still capturing enough information for performance predictions.