Business Intelligence for the Real-Time Enterprise: Second by Alexander Löser, Fabian Hueske, Volker Markl (auth.), Malu

By Alexander Löser, Fabian Hueske, Volker Markl (auth.), Malu Castellanos, Umesh Dayal, Timos Sellis (eds.)

This booklet constitutes the completely refereed post-conference complaints of the second one foreign Workshop on enterprise Intelligence for the Real-Time firm, BIRTE 2008, held in Auckland, New Zealand, in August 2008, at the side of VLDB 2008, the foreign convention on Very huge info Bases.

The keynote speak and the ten revised complete papers offered have been rigorously reviewed and chosen for inclusion within the e-book. The papers talk about the main features of commercial intelligence for the real-time company, targeting versions and ideas, architectures, case-studies, and purposes of and applied sciences for real-time company company intelligence.

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Additional info for Business Intelligence for the Real-Time Enterprise: Second International Workshop, BIRTE 2008, Auckland, New Zealand, August 24, 2008, Revised Selected Papers

Example text

Unlike to the problem for chain query workloads in which we cannot reduce the search space to the class of chain views, for pathquery workloads we can reduce the search space even more. The main result of this section, presented by the following theorem, is that whenever the workload is a set of path-queries, we can focus on path-viewsets whose views have at most as many subgoals as the length of the longest path-query in the workload. Theorem 2. Let P = (S, Q, D, L), be a conjunctive bag-oriented view selection input, and Q contains a set of path queries.

C Springer-Verlag Berlin Heidelberg 2009 46 T. Tully Consider the amount of data that Yahoo! has to collect on a daily basis. We have thousands of web servers to collect log data from, and many of these machines host multiple properties as well as the international versions of those properties. Moreover, we are collecting data for hundreds of millions of users, spanning 5,000,000 log files each day. The data comes from colos (colocated data centers) around the world, totaling a volume of 10TB per day of data, which is stored into one of our 300TB data warehouses2.

I = i + 1. end while 4. ,i Gj . 5. Compute the cost C(Q, D) of Q on D and set it to Copt . 6. For every viewset V ∈ V, such that size(V ) ≤ L, do - Construct the set RV of all equivalent rewritings of Q using V . - Set Λ = ∅. - For every distinct subset R of RV such that R contains an equivalent rewriting of each query in Q, do - Let c = C(R, V (D)). - If c < Copt , then set Copt = c and set Λ = {(V , R)} else if c = Copt , then Λ = Λ ∪ {(V , R)}. end. 5 Chain and Path Queries In this section, we study the bag-oriented view selection problem when the query workload is a set of either chain queries or path queries.

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