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Scalable graph-based OLAP analytics over process execution data

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Abstract

In today’s knowledge-, service-, and cloud-based economy, businesses accumulate massive amounts of data from a variety of sources. In order to understand businesses one may need to perform considerable analytics over large hybrid collections of heterogeneous and partially unstructured data that is captured related to the process execution. This data, usually modeled as graphs, increasingly come to show all the typical properties of big data: wide physical distribution, diversity of formats, non-standard data models, independently-managed and heterogeneous semantics. We use the term big process graph to refer to such large hybrid collections of heterogeneous and partially unstructured process related execution data. Online analytical processing (OLAP) of big process graph is challenging as the extension of existing OLAP techniques to analysis of graphs is not straightforward. Moreover, process data analysis methods should be capable of processing and querying large amount of data effectively and efficiently, and therefore have to be able to scale well with the infrastructure’s scale. While traditional analytics solutions (relational DBs, data warehouses and OLAP), do a great job in collecting data and providing answers on known questions, key business insights remain hidden in the interactions among objects: it will be hard to discover concept hierarchies for entities based on both data objects and their interactions in process graphs. In this paper, we introduce a framework and a set of methods to support scalable graph-based OLAP analytics over process execution data. The goal is to facilitate the analytics over big process graph through summarizing the process graph and providing multiple views at different granularity. To achieve this goal, we present a model for process OLAP (P-OLAP) and define OLAP specific abstractions in process context such as process cubes, dimensions, and cells. We present a MapReduce-based graph processing engine, to support big data analytics over process graphs. We have implemented the P-OLAP framework and integrated it into our existing process data analytics platform, ProcessAtlas, which introduces a scalable architecture for querying, exploration and analysis of large process data. We report on experiments performed on both synthetic and real-world datasets that show the viability and efficiency of the approach.

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  1. http://hadoop.apache.org/.

  2. http://scienceblogs.com/.

  3. http://arxiv.org/.

  4. http://www.myexperiment.org/.

  5. https://www.box.com/.

  6. Organization such as International Federation of Library Associations and Institutions (IFLA)) initiated processes to collect and organize literature and information (http://www.ifla.org/).

  7. http://dblp.uni-trier.de/db/.

  8. http://snap.stanford.edu/data/amazon-meta.html.

  9. An Iceberg-Cube contains only those cells of the data cube that meet an aggregate condition. It is called an Iceberg-Cube because it contains only some of the cells of the full cube [23].

  10. Skyline [25] has been proposed as an important operator for multi-criteria decision making, data mining and visualization, and user preference queries.

  11. An information-network [46] is a network where each node represents an entity (which may have attributes, labels, and weights) and each link (which may have rich semantic information) represents a relationship between two entities.

  12. ProM is the world-leading process mining toolkit. It is an extensible framework that supports a wide variety of process mining techniques in the form of plug-ins.

  13. Linked Data is a method of publishing data on the web based on principles that significantly enhance the adaptability and usability of data, either by humans or machines [24].

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Authors and Affiliations

  1. School of Computer Science and Engineering, University of New South Wales, Sydney, Australia

    Seyed-Mehdi-Reza Beheshti, Boualem Benatallah & Hamid Reza Motahari-Nezhad

  2. IBM Almaden Research Center, San Jose, CA, USA

    Hamid Reza Motahari-Nezhad

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  1. Seyed-Mehdi-Reza Beheshti
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Beheshti, SMR., Benatallah, B. & Motahari-Nezhad, H.R. Scalable graph-based OLAP analytics over process execution data. Distrib Parallel Databases 34, 379–423 (2016). https://doi.org/10.1007/s10619-014-7171-9

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