571 research outputs found

    Technique for anchoring fasteners to honeycomb panels

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    Two-piece fastener bushing provides mounting surface for components on a three-inch thick honeycomb structure. Specially constructed starter drill and sheet metal drill permit drilling without misalignment. Tapered knife-edge cutting tool removes honeycomb core material without tearing the adjacent material

    LINVIEW: Incremental View Maintenance for Complex Analytical Queries

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    Many analytics tasks and machine learning problems can be naturally expressed by iterative linear algebra programs. In this paper, we study the incremental view maintenance problem for such complex analytical queries. We develop a framework, called LINVIEW, for capturing deltas of linear algebra programs and understanding their computational cost. Linear algebra operations tend to cause an avalanche effect where even very local changes to the input matrices spread out and infect all of the intermediate results and the final view, causing incremental view maintenance to lose its performance benefit over re-evaluation. We develop techniques based on matrix factorizations to contain such epidemics of change. As a consequence, our techniques make incremental view maintenance of linear algebra practical and usually substantially cheaper than re-evaluation. We show, both analytically and experimentally, the usefulness of these techniques when applied to standard analytics tasks. Our evaluation demonstrates the efficiency of LINVIEW in generating parallel incremental programs that outperform re-evaluation techniques by more than an order of magnitude.Comment: 14 pages, SIGMO

    Mapping Information Literacy using the Business Research Competencies

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    Purpose Librarians in higher education have adopted curriculum mapping in an effort to determine where effective information literacy instruction can help fill gaps in curriculum and prepare students both for coursework and for future research demands. While curriculum mapping has been utilized widely across academia, few studies have considered business curriculum and the development of information literacy instruction. This paper will provide an overview of the current landscape of curriculum mapping across business courses at two institutions and will provide a replicable methodology for other institutions. Design/methodology/approach In this paper, the authors will examine two case studies at large research universities that evaluate curriculum mapping against the BRASS Business Research Competencies at the undergraduate and the graduate business levels respectively. Findings This study found that the Business Research Competencies were a valid method to evaluate in both case studies. Curriculum mapping also uncovered various gaps in business education across the curricula at both institutions and has led to open discussions with faculty in an effort to improve the success of students both during their degree programs as well as into their careers. Originality/value This study provides a framework and methodology for evaluating business curriculums against robust standards in order to improve student success. With examples from undergraduate and graduate programs, the results of this project promise to have long-lasting implications on the development of curriculums across business programs, including the value of librarian support in developing business research competencies

    Scalable data management in distributed information systems

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    [EN] In the era of cloud computing and huge information systems, distributed applications should manage dynamic workloads; i.e., the amount of client requests per time unit may vary frequently and servers should rapidly adapt their computing efforts to those workloads. Cloud systems provide a solid basis for this kind of applications but most of the traditional relational database systems are unprepared to scale up with this kind of distributed systems. This paper surveys different techniques being used in modern SQL, NoSQL and NewSQL systems in order to increase the scalability and adaptability in the management of persistent data. © 2011 Springer-Verlag.This work has been supported by EU FEDER and Spanish MICINN under research grants TIN2009-14460-C03-01 and TIN2010-17193Pallardó Lozoya, MR.; Esparza Peidro, J.; García Escriva, JR.; Decker, H.; Muñoz Escoí, FD. (2011). Scalable data management in distributed information systems. 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Bull. 32, 36–43 (2009)DeCandia, G., Hastorun, D., Jampani, M., Kakulapati, G., Lakshman, A., Pilchin, A., Sivasubramanian, S., Vosshall, P., Vogels, W.: Dynamo: Amazon’s highly available key-value store. In: 21st ACM Symp. on Operat. Syst. Princ. (SOSP), Stevenson, Washington, USA, pp. 205–220 (2007)Stonebraker, M., Madden, S., Abadi, D.J., Harizopoulos, S., Hachem, N., Helland, P.: The end of an architectural era (it’s time for a complete rewrite). In: 33rd Intnl. Conf. on Very Large Data Bases (VLDB), pp. 1150–1160. ACM Press, Vienna (2007)Lomet, D.B., Fekete, A., Weikum, G., Zwilling, M.J.: Unbundling transaction services in the cloud. In: 4th Biennial Conf. on Innov. Data Syst. Research (CIDR), Asilomar, CA, USA (2009)Campbell, D.G., Kakivaya, G., Ellis, N.: Extreme scale with full SQL language support in Microsoft SQL Azure. In: Intnl. Conf. on Mngmnt. of Data (SIGMOD), pp. 1021–1024. 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    Chiller: Contention-centric Transaction Execution and Data Partitioning for Modern Networks

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    Distributed transactions on high-overhead TCP/IP-based networks were conventionally considered to be prohibitively expensive and thus were avoided at all costs. To that end, the primary goal of almost any existing partitioning scheme is to minimize the number of cross-partition transactions. However, with the new generation of fast RDMA-enabled networks, this assumption is no longer valid. In fact, recent work has shown that distributed databases can scale even when the majority of transactions are cross-partition. In this paper, we first make the case that the new bottleneck which hinders truly scalable transaction processing in modern RDMA-enabled databases is data contention, and that optimizing for data contention leads to different partitioning layouts than optimizing for the number of distributed transactions. We then present Chiller, a new approach to data partitioning and transaction execution, which aims to minimize data contention for both local and distributed transactions. Finally, we evaluate Chiller using various workloads, and show that our partitioning and execution strategy outperforms traditional partitioning techniques which try to avoid distributed transactions, by up to a factor of 2

    A grid-based infrastructure for distributed retrieval

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    In large-scale distributed retrieval, challenges of latency, heterogeneity, and dynamicity emphasise the importance of infrastructural support in reducing the development costs of state-of-the-art solutions. We present a service-based infrastructure for distributed retrieval which blends middleware facilities and a design framework to ‘lift’ the resource sharing approach and the computational services of a European Grid platform into the domain of e-Science applications. In this paper, we give an overview of the DILIGENT Search Framework and illustrate its exploitation in the field of Earth Science

    Parallel Evaluation of Multi-join Queries

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    A number of execution strategies for parallel evaluation of multi-join queries have been proposed in the literature. In this paper we give a comparative performance evaluation of four execution strategies by implementing all of them on the same parallel database system, PRISMA/DB. Experiments have been done up to 80 processors. These strategies, coming from the literature, are named: Sequential Parallel, Synchronous Execution, Segmented Right-Deep, and Full Parallel. Based on the experiments clear guidelines are given when to use which strategy. This is an extended abstract; the full paper appeared in Proc. ACM SIGMOD'94, Minneapolis, Minnesota, May 24–27, 199

    Benchmarking Big Data OLAP NoSQL Databases

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    With the advent of Big Data, new challenges have emerged regarding the evaluation of decision support systems (DSS). Existing evaluation benchmarks are not configured to handle a massive data volume and wide data diversity. In this paper, we introduce a new DSS benchmark that supports multiple data storage systems, such as relational and Not Only SQL (NoSQL) systems. Our scheme recognizes numerous data models (snowflake, star and flat topologies) and several data formats (CSV, JSON, TBL, XML, etc.). It entails complex data generation characterized within “volume, variety, and velocity” framework (3 V). Next, our scheme enables distributed and parallel data generation. Furthermore, we exhibit some experimental results with KoalaBench
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