Database and Expert Systems Applications: 26th International by Qiming Chen, Abdelkader Hameurlain, Farouk Toumani, Roland PDF

By Qiming Chen, Abdelkader Hameurlain, Farouk Toumani, Roland Wagner, Hendrik Decker

This quantity set LNCS 9261 and LNCS 9262 constitutes the refereed lawsuits of the twenty sixth overseas convention on Database and specialist structures purposes, DEXA 2015, held in Valencia, Spain, September 1-4, 2015.

The forty revised complete papers awarded including 32 brief papers, and a pair of keynote talks, have been rigorously reviewed and chosen from a hundred twenty five submissions. The papers talk about a variety of issues together with: temporal, spatial and excessive dimensional databases; semantic internet and ontologies; modeling, associated open facts; NoSQLm NewSQL, facts integration; doubtful facts and inconsistency tolerance; database approach structure; info mining, question processing and optimization; indexing and choice aid structures; modeling, extraction, social networks; wisdom administration and consistency; mobility, privateness and safety; info streams, internet prone; disbursed, parallel and cloud databases; info retrieval; XML and semi-structured info; info partitioning, indexing; information mining, functions; WWW and databases; information administration algorithms.

These volumes additionally contain approved papers of the eighth foreign convention on facts administration in Cloud, Grid and P2P platforms, Globe 2015, held in Valencia, Spain, September 2, 2015. The eight complete papers offered have been conscientiously reviewed and chosen from thirteen submissions. The papers speak about a number issues together with: MapReduce framework: load balancing, optimization and type; safeguard, facts privateness and consistency; question rewriting and streaming.

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Additional info for Database and Expert Systems Applications: 26th International Conference, DEXA 2015, Valencia, Spain, September 1-4, 2015, Proceedings, Part I

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Each experiment was run for 100 randomly selected s-g pairs, and the average time was taken. Varying Query Time Span. In Fig. 1(a), we show the average running times for time spans tβ − tα = 80, 100, 120, 140, 160, 180, 200 and fixed penalty restriction P = 150. In terms of performance, Algorithm DT ime shows a linear progression with increasing time span. Running time of Algorithms DLen and DP en stay stable, which supports their theoretical time bounds. Also, as expected from the theoretical bounds, the running time of DT ime is lower than both DLen and DP en for lower time span values.

Biswas et al. Algorithm 3. 1: procedure A∗ (s, g, tα , tβ , P, len, pen, start, end, hlen , hpen ) 2: closed(v), open(v) ← ∅, for all v ∈ V; 3: add 0, 0, tα , ∅, ∅ to open(s); add s to openV ertices keyed by hlen (s); 4: while (openV ertices is not empty) do 5: remove the vertex u from openV ertices with minimum key, with ties broken arbitrarily, but always in favour of vertex g; remove the minimum length tuple x∗ = , p, t, x, e from open(u); add x∗ to closed(u); 6: if open(u) is not empty, then add u to openV ertices keyed by min +hlen (u), where min = min{ | , p , t , x , e ∈ open(u)}; 7: if (u = g) then construct and return the s-g path having length and penalty p, which arrives at g at end(e); 8: end if 9: for (each edge e+ = (u, v) ∈ δ + (u)) do 10: let p+ = p + pen(e+ ), + = + len(e+ ); x = + , p+ , end(e+ ), x∗ , e+ ; 11: if (p+ + hpen (v) ≤ P, t ≤ start(e+ ) and end(e+ ) ≤ tβ ) then 12: if (there is no tuple in closed(v) that dominates x ) then 13: if (there is no tuple in open(v)) then add x to open(v); 14: add v to openV ertices keyed by + + hlen (v); 15: else if (no triplet in open(v) dominates x ) then 16: remove the triplets from open(v) that are dominated by x ; add x to open(v); update the key of v in openV ertices with + min + hlen (v), where + = min{ | , p , t , x , e ∈ open(v)}; min 17: end if 18: end if 19: end if 20: end for 21: end while 22: return ∅; 23: end procedure Remark 1.

4, we present an FPTAS. Simulation results of the exact algorithms are presented in Sect. 5. Finally, we conclude the paper in Sect. 6. 2 Exact Algorithms Using Dynamic Programming In this section, we present several (pseudo-polynomial) algorithms based on dynamic programming. In Sect. , tα and tβ . In Sect. 2, we present an algorithm whose complexity depends on the query time span. 1 Query Time Span Independent Algorithms Our approach is to find the minimum penalty by which one can reach a vertex v from s, using a temporal path of length that arrives at v at time t, where t ∈ [tα , tβ ].

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