Elisa Turricchia

OLAP query reformulation in peer-to-peer data warehousing

Inter-business collaborative contexts prefigure a distributed scenario where companies organize and coordinate themselves to develop common and shared opportunities, but traditional business intelligence systems do not provide support to this end. To fill this gap, in this paper we envision a peer-to-peer data warehousing architecture based on a network of heterogeneous peers, each exposing query answering functionalities aimed at sharing business information. To enhance the decision making process, an OLAP query expressed on a peer needs to be properly reformulated on the local multidimensional schemata of the other peers. To this end, we present a language for the definition of mappings between the multidimensional schemata of peers and we introduce a query reformulation framework that relies on the translation of mappings, queries, and multidimensional schemata onto the relational level. Then, we formalize a query reformulation algorithm and prove two properties: correctness and closure, that are essential in a peer-to-peer setting. Finally, we discuss the main implementation issues related to the reformulation setting proposed, with specific reference to the case in which the local multidimensional engines hosted by peers use the standard MDX language.

Mining preferences from OLAP query logs for proactive personalization

The goal of personalization is to deliver information that is relevant to an individual or a group of individuals in the most appropriate format and layout. In the OLAP context personalization is quite beneficial, because queries can be very complex and they may return huge amounts of data. Aimed at making the users experience with OLAP as plain as possible, in this paper we propose a proactive approach that couples an MDX-based language for expressing OLAP preferences to a mining technique for automatically deriving preferences. First, the log of past MDX queries issued by that user is mined to extract a set of association rules that relate sets of frequent query fragments; then, given a specific query, a subset of pertinent and effective rules is selected; finally, the selected rules are translated into a preference that is used to annotate the users query. A set of experimental results proves the effectiveness and efficiency of our approach.

Similarity measures for OLAP sessions

OLAP queries are not normally formulated in isolation, but in the form of sequences called OLAP sessions. Recognizing that two OLAP sessions are similar would be useful for different applications, such as query recommendation and personalization; however, the problem of measuring OLAP session similarity has not been studied so far. In this paper, we aim at filling this gap. First, we propose a set of similarity criteria derived from a user study conducted with a set of OLAP practitioners and researchers. Then, we propose a function for estimating the similarity between OLAP queries based on three components: the query group-by set, its selection predicate, and the measures required in output. To assess the similarity of OLAP sessions, we investigate the feasibility of extending four popular methods for measuring similarity, namely the Levenshtein distance, the Dice coefficient, the tf–idf weight, and the Smith–Waterman algorithm. Finally, we experimentally compare these four extensions to show that the Smith–Waterman extension is the one that best captures the users’ criteria for session similarity.

Modern software engineering methodologies meet data warehouse design: 4WD

Data warehouse systems are characterized by a long and expensive development process that hardly meets the ambitious requirements of todays market. This suggests that some further investigation on the methodological issues related to data warehouse design is necessary, aimed at improving the development process from different points of view. In this paper we analyze the potential advantages arising from the application of modern software engineering methodologies to a data warehouse project and we propose 4WD, a design methodology that couples the main principles emerging from these methodologies to the peculiarities of data warehouse projects. The principles underlying 4WD are risk-based iteration, evolutionary and incremental prototyping, user involvement, component reuse, formal and light documentation, and automated schema transformation.

Sprint planning optimization in agile data warehouse design

Agile methods have been increasingly adopted to make data warehouse design faster and nimbler. They divide a data warehouse project into sprints (iterations), and include a sprint planning phase that is critical to ensure the project success. Several factors impact on the optimality of a sprint plan, e.g., the estimated complexity, business value, and affinity of the elemental functionalities included in each sprint, which makes the planning problem difficult. In this paper we formalize the planning problem and propose an optimization model that, given the estimates made by the project team and a set of development constraints, produces an optimal sprint plan that maximizes the business value perceived by users. The planning problem is converted into a multi-knapsack problem with constraints, given a linear programming formulation, and solved using the IBM ILOG CPLEX Optimizer. Finally, the proposed approach is validated through effectiveness and efficiency tests.

Multi-sprint planning and smooth replanning

HighlightsWe propose a model to produce multi-sprint optimal plans for agile projects.Optimal plans maximize the business value perceived by users.Plans can be smoothly revised and re-optimized during project execution.Our model is validated on two case studies and on a set of synthetic projects. Most agile methods divide a project into sprints (iterations), and include a sprint planning phase that is critical to ensure the project success. Several factors impact on the optimality of a sprint plan, which makes the planning problem difficult. In this paper we formalize the planning problem and propose an optimization model that, given the estimates made by the project team and a set of development constraints, produces a multi-sprint optimal plan that maximizes the business value perceived by users. To cope with the inherent flexibility and uncertainty of agile projects, our approach ensures that a baseline plan can be revised and re-optimized during project execution without disrupting it, which we call smooth replanning. The planning problem is converted into a generalized assignment problem, given a linear programming formulation, and solved using the IBM ILOG CPLEX Optimizer. Our model is validated on both real and synthetic projects. In particular, a case study on two real projects confirms the effectiveness of our approach; as to efficiency, for medium-sized problems an exact solution is found in a few minutes, while for large problems a heuristic solution that is less than 1% far from the exact one is returned in a few seconds. Finally, some smooth replanning tests investigate the trade-off between plan quality and stability.

A Lagrangian heuristic for sprint planning in agile software development

Agile methods for software development promote iterative design and implementation. Most of them divide a project into functionalities, called user stories; at each iteration, often called a sprint, a subset of user stories are developed. The sprint planning phase is critical to ensure the project success, but it is also a difficult problem because several factors impact on the optimality of a sprint plan, e.g., the estimated complexity, business value, and affinity of the user stories to be included in each sprint. In this paper we present an approach for sprint planning based on an integer linear programming model. Given the estimates made by the project team and a set of development constraints, the optimal solution of the model is a sprint plan that maximizes the business value perceived by users. Solving to optimality the model by a general-purpose MIP solver, such as IBM Ilog Cplex, takes time and for some instances even finding a feasible solution requires too large computing times for an operational use. For this reason we propose an effective Lagrangian heuristic based on a relaxation of the proposed model and some greedy and exchange algorithms. Computational results on both real and synthetic projects show the effectiveness of the proposed approach.

A characterization of hierarchical computable distance functions for data warehouse systems

Abstract A data warehouse is a huge multidimensional repository used for statistical analysis of historical data. In a data warehouse events are modeled as multidimensional cubes where cells store numerical indicators while dimensions describe the events from different points of view. Dimensions are typically described at different levels of details through hierarchies of concepts. Computing the distance/similarity between two cells has several applications in this domain. In this context distance is typically based on the least common ancestor between attribute values, but the effectiveness of such distance functions varies according to the structure and to the number of the involved hierarchies. In this paper we propose a characterization of hierarchy types based on their structure and expressiveness, we provide a characterization of the different types of distance functions and we verify their effectiveness on different types of hierarchies in terms of their intrinsic discriminant capacity.