Beyond roll-up’s and drill-down’s: An intentional analytics model to reinvent OLAP
This paper structures a novel vision for OLAPby fundamentally redefining several of the pillars on which OLAP has been based for the last 20 years. We redefine OLAP queries, in order to move to higher degrees of abstraction from roll-up’s and drill-down’s, and we propose a set of novel intentional O...
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Veröffentlicht in: | Information systems (Oxford) 2019-11, Vol.85, p.68-91 |
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creator | Vassiliadis, Panos Marcel, Patrick Rizzi, Stefano |
description | This paper structures a novel vision for OLAPby fundamentally redefining several of the pillars on which OLAP has been based for the last 20 years. We redefine OLAP queries, in order to move to higher degrees of abstraction from roll-up’s and drill-down’s, and we propose a set of novel intentional OLAP operators, namely, describe, assess, explain, predict, and suggest, which express the user’s need for results. We fundamentally redefine what a query answer is, and escape from the constraint that the answer is a set of tuples; on the contrary, we complement the set of tuples with models (typically, but not exclusively, results of data mining algorithms over the involved data) that concisely represent the internal structure or correlations of the data. Due to the diverse nature of the involved models, we come up (for the first time ever, to the best of our knowledge) with a unifying framework for them, that places its pillars on the extension of each data cell of a cube with information about the models that pertain to it — practically converting the small parts that build up the models to data that annotate each cell. We exploit this data-to-model mapping to provide highlights of the data, by isolating data and models that maximize the delivery of new information to the user. We introduce a novel method for assessing the surprise that a new query result brings to the user, with respect to the information contained in previous results the user has seen via a new interestingness measure. The individual parts of our proposal are integrated in a new data model for OLAP, which we call the Intentional Analytics Model. We complement our contribution with a list of significant open problems for the community to address.
•We propose a new model for OLAP, which we call the Intentional Analytics Model.•Its operators (describe, assess, explain, predict, suggest) address user intentions.•We redefine what a query answer is: not just tuples, but also models and highlights.•Models (results of mining algorithms) are uniformly modeled a data-to-model mapping.•We introduce a novel method for assessing the surprise of a new query result. |
doi_str_mv | 10.1016/j.is.2019.03.011 |
format | Article |
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•We propose a new model for OLAP, which we call the Intentional Analytics Model.•Its operators (describe, assess, explain, predict, suggest) address user intentions.•We redefine what a query answer is: not just tuples, but also models and highlights.•Models (results of mining algorithms) are uniformly modeled a data-to-model mapping.•We introduce a novel method for assessing the surprise of a new query result.</description><subject>Algorithms</subject><subject>Computer Science</subject><subject>Data mining</subject><subject>Information systems</subject><subject>Mapping</subject><subject>Online analytical processing</subject><issn>0306-4379</issn><issn>1873-6076</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><recordid>eNp1kDFPwzAQhS0EEqWwM0ZiYkg4x7ETdwsVUKRKZQBWy7Ed4SqNi50WdeNv8Pf4JbgqYmM63dP3TvceQpcYMgyY3SwzG7IcMM-AZIDxERrhqiQpg5IdoxEQYGlBSn6KzkJYAkBOOR-h11uzc71OvOu6dLP-_vwKiYy79jYK2n30e2mS1H1i-8H0g3W97CIiu91gVUhWTpsuGVzije23EUgW8_rpHJ20sgvm4neO0cv93fN0ls4XD4_Tep6qAooh5aphtOWtqrjmkBea0qohQFijK2pKpTCjsixZpUgjG2p0pY0EI3lRMBr_J2N0fbj7Jjux9nYl_U44acWsnou9BjmmHGOyxZG9OrBr7943Jgxi6TY-Bgkiz0uCq4oWECk4UMq7ELxp_85iEPumxVLY6IhNCyAiNh0tk4PFxKRba7wIyppeGW29UYPQzv5v_gFvmYX5</recordid><startdate>20191101</startdate><enddate>20191101</enddate><creator>Vassiliadis, Panos</creator><creator>Marcel, Patrick</creator><creator>Rizzi, Stefano</creator><general>Elsevier Ltd</general><general>Elsevier Science Ltd</general><general>Elsevier</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>E3H</scope><scope>F2A</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>1XC</scope><orcidid>https://orcid.org/0000-0003-0085-6776</orcidid><orcidid>https://orcid.org/0000-0002-4617-217X</orcidid><orcidid>https://orcid.org/0000-0003-3171-1174</orcidid></search><sort><creationdate>20191101</creationdate><title>Beyond roll-up’s and drill-down’s: An intentional analytics model to reinvent OLAP</title><author>Vassiliadis, Panos ; Marcel, Patrick ; Rizzi, Stefano</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c404t-9cb65f9fc89d9024d558b3036bd85e7cc165a7768c3bab5ed8dea0ea944655993</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Algorithms</topic><topic>Computer Science</topic><topic>Data mining</topic><topic>Information systems</topic><topic>Mapping</topic><topic>Online analytical processing</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Vassiliadis, Panos</creatorcontrib><creatorcontrib>Marcel, Patrick</creatorcontrib><creatorcontrib>Rizzi, Stefano</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>Library & Information Sciences Abstracts (LISA)</collection><collection>Library & Information Science Abstracts (LISA)</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Hyper Article en Ligne (HAL)</collection><jtitle>Information systems (Oxford)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Vassiliadis, Panos</au><au>Marcel, Patrick</au><au>Rizzi, Stefano</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Beyond roll-up’s and drill-down’s: An intentional analytics model to reinvent OLAP</atitle><jtitle>Information systems (Oxford)</jtitle><date>2019-11-01</date><risdate>2019</risdate><volume>85</volume><spage>68</spage><epage>91</epage><pages>68-91</pages><issn>0306-4379</issn><eissn>1873-6076</eissn><abstract>This paper structures a novel vision for OLAPby fundamentally redefining several of the pillars on which OLAP has been based for the last 20 years. 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•We propose a new model for OLAP, which we call the Intentional Analytics Model.•Its operators (describe, assess, explain, predict, suggest) address user intentions.•We redefine what a query answer is: not just tuples, but also models and highlights.•Models (results of mining algorithms) are uniformly modeled a data-to-model mapping.•We introduce a novel method for assessing the surprise of a new query result.</abstract><cop>Oxford</cop><pub>Elsevier Ltd</pub><doi>10.1016/j.is.2019.03.011</doi><tpages>24</tpages><orcidid>https://orcid.org/0000-0003-0085-6776</orcidid><orcidid>https://orcid.org/0000-0002-4617-217X</orcidid><orcidid>https://orcid.org/0000-0003-3171-1174</orcidid></addata></record> |
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subjects | Algorithms Computer Science Data mining Information systems Mapping Online analytical processing |
title | Beyond roll-up’s and drill-down’s: An intentional analytics model to reinvent OLAP |
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