Read e-book online Business Intelligence: Third European Summer School, eBISS PDF

By Esteban Zimányi

ISBN-10: 3319054600

ISBN-13: 9783319054605

ISBN-10: 3319054619

ISBN-13: 9783319054612

To huge corporations, company intelligence (BI) delivers the potential of accumulating and interpreting inner and exterior info to generate wisdom and cost, hence delivering selection help on the strategic, tactical, and operational degrees. BI is now impacted via the “Big facts” phenomena and the evolution of society and clients. particularly, BI purposes needs to take care of extra heterogeneous (often Web-based) resources, e.g., from social networks, blogs, competitors’, suppliers’, or vendors’ information, governmental or NGO-based research and papers, or from learn guides. moreover, they need to be ready to offer their effects additionally on cellular units, making an allowance for location-based or time-based environmental data.

The lectures held on the 3rd ecu enterprise Intelligence summer time university (eBISS), that are awarded the following in a longer and subtle structure, conceal not just validated BI and BPM applied sciences, yet expand into leading edge facets which are vital during this new atmosphere and for novel functions, e.g., development and strategy mining, company semantics, associated Open facts, and large-scale information administration and analysis.

Combining papers through top researchers within the box, this quantity equips the reader with the cutting-edge history beneficial for growing the way forward for BI. It additionally offers the reader with a superb foundation and plenty of guidelines for additional examine during this transforming into field.

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Read or Download Business Intelligence: Third European Summer School, eBISS 2013, Dagstuhl Castle, Germany, July 7-12, 2013, Tutorial Lectures PDF

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Additional info for Business Intelligence: Third European Summer School, eBISS 2013, Dagstuhl Castle, Germany, July 7-12, 2013, Tutorial Lectures

Example text

Definition 1 (Function Projection). Let f ⊆ X ≈⇔ Y be a (partial) function and Q ⇒ X. f Q is the function projected on Q: dom(f Q ) = dom(f ) ∩ Q and f Q (x) = f (x) for x ⊆ dom(f Q ). , [x3 , y, z 2 ] {x,y} = [x3 , y]. is the σ = a1 , a2 , . . , an ⊆ X ∗ denotes a sequence over X of length n. empty sequence. mults k (σ) = [a1 , a2 , . . , ak ] is the multiset composed of the first k elements of σ. mults(σ) = mults |σ| (σ) converts a sequence into a multiset. mults 2 ( a, a, b, a, b ) = [a2 ] and mults( a, a, b, a, b ) = [a3 , b2 ].

3 Preliminaries This section introduces basic concepts related to Petri nets and event logs. 1 Multisets, Functions, and Sequences Multisets are used to represent the state of a Petri net and to describe event logs where the same trace may appear multiple times. B(A) is the set of all multisets over some set A. For some multiset B ⊆ B(A), B(a) denotes the number of times element a ⊆ A appears in B. Some examples: B1 = [ ], B2 = [x, x, y], B3 = [x, y, z], B4 = [x, x, y, x, y, z], B5 = [x3 , y 2 , z] are multisets over A = {x, y, z}.

Springer, New york (2010) 2. : Mining association rules between sets of items in large databases. ACM SIGMOD Rec. 22(2), 207–216 (1993) 3. : Fast algorithms for mining association rules. In: Procedings of 20th International Conference on Very Large Data Bases, VLDB, vol. 1215, pp. 487–499 (1994) 4. : SD-Map–a fast algorithm for exhaustive subgroup discovery. In: Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases - ECML PKDD, pp. 6–17. Springer (2006) 5.

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Business Intelligence: Third European Summer School, eBISS 2013, Dagstuhl Castle, Germany, July 7-12, 2013, Tutorial Lectures by Esteban Zimányi


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