OLAP queries context-aware recommender system

It becomes hard and tedious to easily obtain relevant decisional data in large data warehouses. In order to ease user exploration during on-line analytical processing analysis, recommender systems are developed. However some recommendations can be inappropriate (irrelevant queries or non-computable queries). To overcome these mismatches, we propose to integrate contextual data into the recommender system. In this paper, we provide (i) an indicator of obsolescence for OLAP queries and (ii) a context-aware recommender system based on a contextual post-filtering for OLAP queries

A Holistic Approach to OLAP Sessions Composition: The Falseto Experience

International audienceOLAP is the main paradigm for flexible and effective exploration of multidimensional cubes in data warehouses. During an OLAP session the user analyzes the results of a query and determines a new query that will give her a better understanding of information. Given the huge size of the data space, this exploration process is often tedious and may leave the user disoriented and frustrated. This paper presents an OLAP tool 1 named Falseto (Former AnalyticaL Sessions for lEss Tedious Olap), that is meant to assist query and session composition, by letting the user summarize, browse, query, and reuse former analytical sessions. Falseto's implementation on top of a formal framework is detailed. We also report the experiments we run to obtain and analyze real OLAP sessions and assess Falseto with them. Finally, we discuss how Falseto can be seen as a starting point for bridging OLAP with exploratory search, a search paradigm centered on the user and the evolution of her knowledge

QueRIE: Collaborative Database Exploration

Interactive database exploration is a key task in information mining. However, users who lack SQL expertise or familiarity with the database schema face great difficulties in performing this task. To aid these users, we developed the QueRIE system for personalized query recommendations. QueRIE continuously monitors the user’s querying behavior and finds matching patterns in the system’s query log, in an attempt to identify previous users with similar information needs. Subsequently, QueRIE uses these “similar” users and their queries to recommend queries that the current user may find interesting. In this work we describe an instantiation of the QueRIE framework, where the active user’s session is represented by a set of query fragments. The recorded fragments are used to identify similar query fragments in the previously recorded sessions, which are in turn assembled in potentially interesting queries for the active user. We show through experimentation that the proposed method generates meaningful recommendations on real-life traces from the SkyServer database and propose a scalable design that enables the incremental update of similarities, making real-time computations on large amounts of data feasible. Finally, we compare this fragment-based instantiation with our previously proposed tuple-based instantiation discussing the advantages and disadvantages of each approach

Revisting SQL Query Recommender System Using Hierarchical Classification

For analytical purposes, lots of data are gathered which are gathered and explored in data warehouses. Even to handle such a large data is a tough task for expert people. For non-expert users or for users who are not familiar with the database schema, handling such a voluminous data is more difficult task. The aim of this paper is to facilitate this class of users by recommending them SQL queries that they may use. By following the users past behavior and comparing them with other users, these SQL recommendations are selected. Initially, users may not know from where they can start their exploration. Secondly, users may overlook queries which help them to retrieve important data. Using hierarchical classification, the queries are recorded and compared which is then re-ranked according to relevance. Using users querying behavior, the relevant queries are retrieved. To issue a series of SQL queries, users use a query interface which aim to analyze the data and mine it for interesting information. DOI: 10.17762/ijritcc2321-8169.150614

Graph BI & analytics: current state and future challenges

In an increasingly competitive market, making well-informed decisions requires the analysis of a wide range of heterogeneous, large and complex data. This paper focuses on the emerging field of graph warehousing. Graphs are widespread structures that yield a great expressive power. They are used for modeling highly complex and interconnected domains, and efficiently solving emerging big data application. This paper presents the current status and open challenges of graph BI and analytics, and motivates the need for new warehousing frameworks aware of the topological nature of graphs. We survey the topics of graph modeling, management, processing and analysis in graph warehouses. Then we conclude by discussing future research directions and positioning them within a unified architecture of a graph BI and analytics framework.Peer ReviewedPostprint (author's final draft

A Survey of UserCentric Data Warehouses: From Personalization to Recommendation”, The

ABSTRACT Providing a customized support for the OLAP brings tremendous challenges to the OLAP technology. Standing at the crossroads of the preferences and the data warehouse, two emerging trends are pointed out; namely: (i) the personalization and (ii) the recommendation. Although the panoply of the proposed approaches, the user-centric data warehouse community issues have not been addressed yet. In this paper we draw an overview of several user centric data warehouse proposals. We also discuss the two promising concepts in this issue, namely, the personalization and the recommendation of the data warehouses. We compare the current approaches among each others with respect to some criteria

Predicting your next OLAP query based on recent analytical sessions

International audienceIn Business Intelligence systems, users interact with data warehouses by formulating OLAP queries aimed at exploring multidimensional data cubes. Being able to predict the most likely next queries would provide a way to recommend interesting queries to users on the one hand, and could improve the efficiency of OLAP sessions on the other. In particular, query recommendation would proactively guide users in data exploration and improve the quality of their interactive experience. In this paper, we propose a framework to predict the most likely next query and recommend this to the user. Our framework relies on a probabilistic user behavior model built by analyzing previous OLAP sessions and exploiting a query similarity metric. To gain insight in the recommendation precision and on what parameters it depends, we evaluate our approach using different quality assessments