The knowledge grid

Cannataro, Mario; Talia, Domenico

doi:10.1145/602421.602425

Cited by 232 publications

(79 citation statements)

References 4 publications

(4 reference statements)

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“…5.4, we also plan to have more deeper investigation on how these strategies can be combined together to produce better solutions. In order to further develop these strategies to meet the growing number of data sources, we will develop parallel, distributed implementations of these strategies in a knowledge grid architecture [9]. Currently, we just carried out a set of experiments in the context of query refinement and processing on large-scale scientific literatures.…”

Section: Discussionmentioning

confidence: 99%

User-centric query refinement and processing using granularity-based strategies

et al. 2010

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Under the context of large-scale scientific literatures, this paper provides a usercentric approach for refining and processing incomplete or vague query based on cognitiveand granularity-based strategies. From the viewpoints of user interests retention and granular information processing, we examine various strategies for user-centric unification of search and reasoning. Inspired by the basic level for human problem-solving in cognitive science, we refine a query based on retained user interests. We bring the multi-level, multi-perspective strategies from human problem-solving to large-scale search and reasoning. The power/ exponential law-based interests retention modeling, network statistics-based data selection, and ontology-supervised hierarchical reasoning are developed to implement these strategies. As an illustration, we investigate some case studies based on a large-scale scientific literature dataset, DBLP. The experimental results show that the proposed strategies are potentially effective.

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Section: Discussionmentioning

confidence: 99%

User-centric query refinement and processing using granularity-based strategies

et al. 2010

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“…12, this kind of information needs to be managed by knowledge capture agents. Parameters enabling the engine to trace the original data should constitute a kind of analytical knowledge grid, by analogy to the concept of knowledge grid considered in semantic web (Cannataro and Talia 2003).…”

Section: Data Types and Derived Columnsmentioning

confidence: 99%

A new approximate query engine based on intelligent capture and fast transformations of granulated data summaries

Ślęzak

Glick

Betliński³

et al. 2017

J Intell Inf Syst

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We outline the processes of intelligent creation and utilization of granulated data summaries in the engine aimed at fast approximate execution of analytical SQL statements. We discuss how to use the introduced engine for the purposes of ad-hoc data exploration over large and quickly increasing data collected in a heterogeneous or distributed fashion. We focus on mechanisms that transform input data summaries into result sets representing query outcomes. We also illustrate how our computational principles can be put together with other paradigms of scaling and harnessing data analytics.

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“…Knowledge Gird (KG) is an integration of basic grid services, data grid services and computational grid services for distributed data mining and knowledge discovery [10,12] . We built Knowledge Grid using the open-source globus toolkit [9] .…”

Section: Knowledge Gridmentioning

confidence: 99%

Incremental DataGrid Mining Algorithm for Mobility Prediction of Mobile Users

Sakthi¹

2009

J. of Computer Science

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Problem statement: Mobility prediction is the important issue in Personal Communication Systems (PCS). Mobile users moving logs are stored in data grid located in different locations. Distributed data mining algorithm is applied on this moving logs to generate the mobility pattern of mobile users. As new moving logs are added to the data grid, existing mobility pattern becomes invalid and it should be updated. One of the existing work to derive the new mobility pattern is re-executing the algorithm from scratch results in excessive computation. Approach: We had designed new incremental algorithm by maintaining infrequent mobility patterns, which avoids unnecessary scan of full database. Incremental data mining algorithm taken lesser time to compute new mobility patterns. The discovered location patterns can be used to provide various location based services to the mobile user by the application server in mobile computing environment. Data grid provided geographically distributed database for computational grid which implements incremental data mining algorithm. We built data grid system on a cluster of workstation using open source globus toolkit 4.0 and Message Passing Interface extended with Grid Services (MPICH-G2). Results: The experiments were conducted on original data sets and data were added incrementally and the computation time was recorded for each data sets. The performance improvement for increment size of 100 K was about 55% for 0.20% support count and it is increased to 60% for 0.25% support count. The performance is increased about 65% for the support count 0.30%. Conclusion: We analyzed our results with various sizes of data sets and the proof shows the time taken to generate mobility pattern by incremental mining algorithm is less than re-computing approach. In future the execution time can further be reduced by balancing the workload of grid nodes.

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The knowledge grid

Abstract: Designing, building, and implementing an architecture for distributed knowledge discovery.

Cited by 232 publications

References 4 publications

User-centric query refinement and processing using granularity-based strategies

User-centric query refinement and processing using granularity-based strategies

A new approximate query engine based on intelligent capture and fast transformations of granulated data summaries

Incremental DataGrid Mining Algorithm for Mobility Prediction of Mobile Users

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