Extended drill‐down operator: Digging into the structure of performance indicators

Performance measurement is the subject of interdisciplinary research on information systems, organizational modeling and decision support systems. The data cube model is usually adopted to represent performance indicators (PI) and enable flexible analysis, visualization and reporting. However, the major obstacles against effective design and management of PI monitoring systems are related to the facts that PIs are complex objects with an aggregate/compound nature. This often leads to unawareness of indicator semantics as well as of dependencies among indicators. In this work, we propose to enrich the data cube model with the formal description of the structure of an indicator given in terms of its algebraic formula and aggregation function. Such a model enables the definition of a novel operator, namely indicator drill‐down, which relies on formula manipulation functionalities and reasoning. Like the usual drill‐down, this operator increases the detail of a measure of the data cube by expanding an indicator into its components. Thus, the two notions of drill‐down are integrated, allowing a novel way of data exploration. As a proof‐of‐concept, an implementation of the approach is presented. The evaluation of the implementation on real and synthetic scenarios enlightens the effectiveness and the efficiency of the approach. Copyright © 2015 John Wiley & Sons, Ltd.

[1]  Frank Leymann,et al.  Towards Measuring Key Performance Indicators of Semantic Business Processes , 2008, BIS.

[2]  Günther Pernul,et al.  Ontology-based integration of OLAP and information retrieval , 2003, 14th International Workshop on Database and Expert Systems Applications, 2003. Proceedings..

[3]  Panagiotis Chytas,et al.  A proactive balanced scorecard , 2011, Int. J. Inf. Manag..

[4]  Michael H. Breitner,et al.  Ontology-Based Exchange and Immediate Application of Business Calculation Definitions for Online Analytical Processing , 2009, DaWaK.

[5]  Matteo Golfarelli,et al.  OLAP query reformulation in peer-to-peer data warehousing , 2012, Inf. Syst..

[6]  Laks V. S. Lakshmanan,et al.  Efficacious Data Cube Exploration by Semantic Summarization and Compression , 2003, VLDB.

[7]  Claudia Diamantini,et al.  Extending Drill-Down through Semantic Reasoning on Indicator Formulas , 2014, DaWaK.

[8]  Bernd Neumayr,et al.  Multi-level Conceptual Modeling and OWL , 2009, ER Workshops.

[9]  Shengping Liu,et al.  EIAW: Towards a Business-Friendly Data Warehouse Using Semantic Web Technologies , 2007, ISWC/ASWC.

[10]  Torben Bach Pedersen,et al.  Using Semantic Web Technologies for Exploratory OLAP: A Survey , 2015, IEEE Transactions on Knowledge and Data Engineering.

[11]  Jacky Akoka,et al.  Multidimensional models meet the semantic web: defining and reasoning on OWL-DL ontologies for OLAP , 2012, DOLAP '12.

[12]  Claudia Diamantini,et al.  Data Mart Reconciliation in Virtual Innovation Factories , 2014, CAiSE Workshops.

[13]  John Mylopoulos,et al.  Reasoning with Key Performance Indicators , 2011, PoEM.

[14]  S.D.P. Flapper,et al.  Towards consistent performance management systems , 1996 .

[15]  Henry Chesbrough,et al.  Open Innovation: The New Imperative for Creating and Profiting from Technology , 2003 .

[16]  Jia-Lang Seng,et al.  Data warehouse enhancement: A semantic cube model approach , 2007, Inf. Sci..

[17]  Sushil,et al.  Performance measurement and management frameworks: Research trends of the last two decades , 2013, Bus. Process. Manag. J..

[18]  Zahir Tari,et al.  On the Move to Meaningful Internet Systems. OTM 2018 Conferences , 2018, Lecture Notes in Computer Science.

[19]  Leon Sterling,et al.  Solving Symbolic Equations with PRESS , 1989, J. Symb. Comput..

[20]  John Domingue,et al.  Ontology-based metrics computation for business process analysis , 2009, SBPM '09.

[21]  Torben Bach Pedersen,et al.  SM4AM: A Semantic Metamodel for Analytical Metadata , 2014, DOLAP '14.

[22]  R. Kaplan,et al.  The balanced scorecard--measures that drive performance. , 2015, Harvard business review.

[23]  John Mylopoulos,et al.  Strategic business modeling: representation and reasoning , 2014, Software & Systems Modeling.

[24]  Hamid Pirahesh,et al.  Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Totals , 1996, Data Mining and Knowledge Discovery.

[25]  Manuel Resinas,et al.  Defining Process Performance Indicators: An Ontological Approach , 2010, OTM Conferences.

[26]  Haotian Zhang,et al.  An Ontology-Based Data Exploration Tool for Key Performance Indicators , 2014, OTM Conferences.

[27]  Jyrki Nummenmaa,et al.  Ontologies with Semantic Web/Grid in Data Integration for OLAP , 2007, Int. J. Semantic Web Inf. Syst..

[28]  Torben Bach Pedersen,et al.  The Meta-Morphing Model Used in TARGIT BI Suite , 2011, ER Workshops.

[29]  Viara Popova,et al.  Modeling organizational performance indicators , 2010, Inf. Syst..

[30]  Maurizio Proietti,et al.  A Semantic Framework for Knowledge Management in Virtual Innovation Factories , 2013, Int. J. Inf. Syst. Model. Des..

[31]  Lorena Etcheverry,et al.  Modeling and Querying Data Warehouses on the Semantic Web Using QB4OLAP , 2014, DaWaK.

[32]  Bernd Neumayr,et al.  Towards ontology-based OLAP: datalog-based reasoning over multidimensional ontologies , 2012, DOLAP '12.