Download Business Intelligence: Third European Summer School, eBISS by Esteban Zimányi PDF

By Esteban Zimányi

To huge organisations, company intelligence (BI) supplies the potential of gathering and studying inner and exterior information to generate wisdom and price, hence delivering determination aid on the strategic, tactical, and operational degrees. BI is now impacted via the “Big information” phenomena and the evolution of society and clients. specifically, BI purposes needs to focus on extra heterogeneous (often Web-based) assets, e.g., from social networks, blogs, competitors’, suppliers’, or vendors’ info, governmental or NGO-based research and papers, or from study guides. moreover, they have to be capable of supply their effects additionally on cellular units, considering location-based or time-based environmental data.

The lectures held on the 3rd eu company Intelligence summer time university (eBISS), that are offered the following in a longer and subtle layout, hide not just tested BI and BPM applied sciences, yet expand into cutting edge points which are very important during this new surroundings and for novel purposes, e.g., trend and method mining, enterprise semantics, associated Open information, and large-scale information administration and analysis.

Combining papers through major researchers within the box, this quantity equips the reader with the state of the art history invaluable for growing the way forward for BI. It additionally offers the reader with a good foundation and lots of tips for additional examine during this growing to be field.

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

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X ⊆ B2 , B2 ∈ B3 = B4 , B5 \ B2 = B3 , |B5 | = 6, etc. {a ⊆ B} denotes the set with all elements a for which B(a) ≥ 1. [f (a) | a ⊆ B] denotes the multiset where element f (a) appears x∈B|f (x)=f (a) B(x) times. P. van der Aalst A relation R ⇒ X ×Y is a set of pairs. π1 (R) = {x | (x, y) ⊆ R} is the domain of R, π2 (R) = {y | (x, y) ⊆ R} is the range of R, and ω(R) = π1 (R) ∪ π2 (R) are the elements of R. For example, ω({(a, b), (b, c)}) = {a, b, c}. , f (x) ⊆ Y for any x ⊆ X. f ⊆ X ≈⇔ Y is a partial function with domain dom(f ) ⇒ X and range rng(f ) = {f (x) | x ⊆ X} ⇒ Y .

This explains the title of this tutorial: “Process Mining in the Large”. The remainder of this paper is organized as follows. Section 2 provides an overview of the process mining spectrum. Some basic notions are introduced in Sect. 3. Section 4 presents two process discovery algorithms: the α-algorithm (Sect. 1) and region-based process discovery (Sect. 2). Section 5 introduces two conformance checking techniques. Moreover, the different quality dimensions are discussed and the importance of aligning observed and modeled behavior is explained.

As shown in Fig. 4, event data can be partitioned into “pre mortem” and “post mortem” event logs. , these data can be used for process improvement and auditing, but not for influencing the cases they refer to. “Pre mortem” event data refer to cases that have not yet completed. , current data) can be exploited to ensure the correct or efficient handling of this case. P. van der Aalst people machines business processes “world” organizations documents information system(s) provenance event logs current data historic data cartography discover enhance promote check compare detect recommend auditing predict explore navigation “post mortem” diagnose “pre mortem” models de jure models de facto models control-flow control-flow data/rules data/rules resources/ organization resources/ organization Fig.

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