Index ordering by query-independent measures

Conventional approaches to information retrieval search through all applicable entries in an inverted file for a particular collection in order to find those documents with the highest scores. For particularly large collections this may be extremely time consuming. A solution to this problem is to only search a limited amount of the collection at query-time, in order to speed up the retrieval process. In doing this we can also limit the loss in retrieval efficacy (in terms of accuracy of results). The way we achieve this is to firstly identify the most “important” documents within the collection, and sort documents within inverted file lists in order of this “importance”. In this way we limit the amount of information to be searched at query time by eliminating documents of lesser importance, which not only makes the search more efficient, but also limits loss in retrieval accuracy. Our experiments, carried out on the TREC Terabyte collection, report significant savings, in terms of number of postings examined, without significant loss of effectiveness when based on several measures of importance used in isolation, and in combination. Our results point to several ways in which the computation cost of searching large collections of documents can be significantly reduced

Comparison of Geometric Optimization Methods with Multiobjective Genetic Algorithms for Solving Integrated Optimal Design Problems

In this paper, system design methodologies for optimizing heterogenous power devices in electrical engineering are investigated. The concept of Integrated Optimal Design (IOD) is presented and a simplified but typical example is given. It consists in finding Pareto-optimal configurations for the motor drive of an electric vehicle. For that purpose, a geometric optimization method (i.e the Hooke and Jeeves minimization procedure) associated with an objective weighting sum and a Multiobjective Genetic Algorithm (i.e. the NSGA-II) are compared. Several performance issues are discussed such as the accuracy in the determination of Pareto-optimal configurations and the capability to well spread these solutions in the objective space

Code Generation for Efficient Query Processing in Managed Runtimes

In this paper we examine opportunities arising from the conver-gence of two trends in data management: in-memory database sys-tems (IMDBs), which have received renewed attention following the availability of affordable, very large main memory systems; and language-integrated query, which transparently integrates database queries with programming languages (thus addressing the famous ‘impedance mismatch ’ problem). Language-integrated query not only gives application developers a more convenient way to query external data sources like IMDBs, but also to use the same querying language to query an application’s in-memory collections. The lat-ter offers further transparency to developers as the query language and all data is represented in the data model of the host program-ming language. However, compared to IMDBs, this additional free-dom comes at a higher cost for query evaluation. Our vision is to improve in-memory query processing of application objects by introducing database technologies to managed runtimes. We focus on querying and we leverage query compilation to im-prove query processing on application objects. We explore dif-ferent query compilation strategies and study how they improve the performance of query processing over application data. We take C] as the host programming language as it supports language-integrated query through the LINQ framework. Our techniques de-liver significant performance improvements over the default LINQ implementation. Our work makes important first steps towards a future where data processing applications will commonly run on machines that can store their entire datasets in-memory, and will be written in a single programming language employing language-integrated query and IMDB-inspired runtimes to provide transparent and highly efficient querying. 1

Datacab: a geographical‐information‐system‐based expert system for the design of cable networks

Telecommunication networks have evolved over time as a result of technological advances, and network topologies and equipment have become increasingly complex. Expert systems are being successfully applied to the management of telecommunication networks. However, applying expert systems to network design is another especially beneficial yet still not very common approach. In this paper we propose a rule‐based expert system called Datacab. Datacab was developed at Enditel Endesa in collaboration with the Electronic Technology Department of the University of Seville, for the automatic design of hybrid fibre coax (HFC) cable networks. Using data from a geographical information system as input, it automatically generates viable HFC network designs

Designing a CPU model: from a pseudo-formal document to fast code

For validating low level embedded software, engineers use simulators that take the real binary as input. Like the real hardware, these full-system simulators are organized as a set of components. The main component is the CPU simulator (ISS), because it is the usual bottleneck for the simulation speed, and its development is a long and repetitive task. Previous work showed that an ISS can be generated from an Architecture Description Language (ADL). In the work reported in this paper, we generate a CPU simulator directly from the pseudo-formal descriptions of the reference manual. For each instruction, we extract the information describing its behavior, its binary encoding, and its assembly syntax. Next, after automatically applying many optimizations on the extracted information, we generate a SystemC/TLM ISS. We also generate tests for the decoder and a formal specification in Coq. Experiments show that the generated ISS is as fast and stable as our previous hand-written ISS.Comment: 3rd Workshop on: Rapid Simulation and Performance Evaluation: Methods and Tools (2011