Bottom-up computation of sparse and Iceberg CUBE
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
A guided tour to approximate string matching
ACM Computing Surveys (CSUR)
Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Totals
Data Mining and Knowledge Discovery
Approximate String Joins in a Database (Almost) for Free
Proceedings of the 27th International Conference on Very Large Data Bases
Efficient set joins on similarity predicates
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
A Primitive Operator for Similarity Joins in Data Cleaning
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Efficient exact set-similarity joins
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Hierarchies in a multidimensional model: from conceptual modeling to logical representation
Data & Knowledge Engineering - Special issue: WIDM 2004
Efficient similarity joins for near duplicate detection
Proceedings of the 17th international conference on World Wide Web
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
Text Cube: Computing IR Measures for Multidimensional Text Database Analysis
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Topic modeling for OLAP on multidimensional text databases: topic cube and its applications
Statistical Analysis and Data Mining - Best of SDM'09
LCI: a social channel analysis platform for live customer intelligence
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Meta-stars: multidimensional modeling for social business intelligence
Proceedings of the sixteenth international workshop on Data warehousing and OLAP
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With the advent of social media there is an ever increasing amount of unstructured data that can be analyzed to obtain insights. Two prominent examples are sentiment analysis and the discovery of correlated concepts. A convenient representation of information in such scenarios is in terms of concepts extracted from the unstructured data, and measures, such as sentiment scores, associated with these concepts. Typically, social media analysis reports these concepts and their associated measures. We argue that much richer insights can be obtained through the use of OLAP-style multidimensional analysis. It is fairly straightforward to see how to add traditional dimension hierarchies such as time and geography, and to analyze the data along these dimensions using traditional OLAP operations such as roll-up; for instance, to answer queries of the form "What was the average sentiment for X in Europe during the past month?" However, it is trickier to answer queries of the form "What was the average sentiment for concepts related to X in Europe during the past month?" We introduce a conceptual modeling framework that extends traditional multidimensional models and OLAP operators to address the new set of requirements for data extracted from social media. In this model, we organize data along both traditional dimensions (we call these metadata dimensions) and concept dimensions, which model relationships among concepts using parent-child hierarchies. Specifically: (i) we allow operations on parent-child hierarchies to be treated in a uniform way as operations on traditional dimension hierarchies; (ii) to model the rich relationships that can exist among concepts, we extend the parent-child hierarchies to be rooted level-DAGs rather than simply trees; and (iii) we introduce new equivalence classes that allow us to reason with "similar" concepts in new ways. We show that our modeling and operator framework facilitates multidimensional analysis to gain further insights from social media data than is possible with existing methods.