In an attempt to streamline exploratory data analysis of multidimensional cubes, the Intentional Analytics Model ha been proposed as a way to unite OLAP and analytics by allowing users to indicate their analysis intentions and returning cubes enhanced with models. Five intention operators were envisioned to this end; in this work we focus on the predict operator, whose goal is to estimate the missing values of a cube measure starting from known values of the same measure or other measures using different regression models. Although prediction tasks such as forecasting and imputation are routinary for analysts, the added value of our approach is (i) to encapsulate them in a declarative, concise, natural language-like syntax; (ii) to automate the selection of the best measures to be used and the computation of the models, and (iii) to automate the evaluation of the interest of the models computed. First we propose a syntax and a semantics for predict and discuss how enhanced cubes are built by (i) predicting the missing values for a measure based on the available information via one or more models and (ii) highlighting the most interesting prediction. Then we test the operator implementation, proving that its performance is in line with the interactivity requirement of OLAP session and that accurate predictions can be returned.

Large Language Models (LLMs) can simulate human linguistic capabilities, thus producing a disruptive impact across several domains, including software engineering. In this paper we focus on a specific scenario of software engineering, that of conceptual design of multidimensional data cubes. The goal is to evaluate the performance of LLMs (precisely, of ChatGPT-4o) in multidimensional conceptual design using the Dimensional Fact Model as a reference. To this end, we formulate nine research questions to (i) understand the competences of ChatGPT in multidimensional conceptual design, following either a supply- or a demand-driven approach, and (ii) investigate to what extent they can be improved via prompt engineering. After describing the research process in terms of base criteria, technological setting, input/output format, prompt templates, test cases, and metrics for evaluating the results, we discuss the output of the experiment. Our main conclusions are that (i) when prompts are enhanced with detailed procedural instructions and examples, the results produced significantly improve in all cases; and (ii) overall, ChatGPT is better at demand-driven design than at supply-driven design.

OLAP streamlines the exploration of multidimensional data cubes by allowing decision-makers to build sessions of analytical queries via a ``point-and-click'' interaction. However, new scenarios are appearing in which alternative forms of user-system communication, based for instance on natural language, are necessary. To cope with these scenarios, we present VOOL, an extensible framework for the vocalization of the results of OLAP sessions. To avoid flooding the user with long and tedious descriptions, we choose to vocalize only selected insights automatically extracted from query results. Insights are quantitative and rich-in-semantics characterizations of the results of an OLAP query, and they also take into account the user's intentions as expressed by OLAP operators. Firstly, they are extracted using statistics and machine learning algorithms; then an optimization algorithm is applied to select the most relevant insights respecting a limit on the overall duration of vocalization. Finally, the selected insights are sorted into a comprehensive description that is vocalized to the user. After describing and formalizing our approach, we evaluate it from the points of view of efficiency, effectiveness, and operativity, also by comparing it with LLM-based applications.

Data platforms are state-of-the-art solutions for implementing data-driven applications and analytics. They facilitate the ingestion, storage, management, and exploitation of big data. Data platforms are built on top of complex ecosystems of services answering different data needs and requirements; such ecosystems are offered by different providers (e.g., Amazon AWS and Microsoft Azure). However, when it comes to engineering data platforms, no unifying strategy and methodology is available yet, and the design is mainly left to the expertise of practitioners in the field. Service providers simply expose a long list of interoperable and alternative engines, making it hard to select the optimal subset without a deep knowledge of the ecosystem. A more effective design approach starts with knowledge of the data transformation and exploitation processes that the platform should support. In this paper, we sketch a computer-aided design methodology and then focus on the selection of the optimal services needed to implement such processes. We show that our approach lightens the design of data platforms and enables an unbiased selection and comparison of solutions even through different service ecosystems. • Automatic design of blueprints of data platforms driven by data processes. • The methodology is composed of three steps: matching, augmentation, and optimization. • The methodology is portable to any cloud service provider. • Recognition of architectural patterns, such as Lakehouse and Lambda Architecture. • Human in the loop: designers can inject their knowledge to refine the blueprints.

The Intentional Analytics Model (IAM) has been devised to couple OLAP and analytics by (i) letting users express their analysis intentions on multidimensional data cubes and (ii) returning enhanced cubes, i.e., multidimensional data annotated with knowledge insights in the form of models (e.g., correlations). Five intention operators were proposed to this end; of these, describe and assess have been investigated in previous papers. In this work we enrich the IAM picture by focusing on the explain operator, whose goal is to provide an answer to the user asking “why does measure m show these values?”; specifically, we consider models that explain m in terms of one or more other measures. We propose a syntax for the operator and discuss how enhanced cubes are built by (i) finding the relationship between m and the other cube measures via regression analysis and cross-correlation, and (ii) highlighting the most interesting one. Finally, we test the operator implementation in terms of efficiency and effectiveness.

Assessment is the process of comparing the actual to the expected behavior of a business phenomenon and judging the outcome of the comparison. The assess querying operator has been recently proposed to support assessment based on the results of a query on a data cube. This operator requires (i) the specification of an OLAP query to determine a target cube; (ii) the specification of a reference cube of comparison (benchmark), which represents the expected performance; (iii) the specification of how to perform the comparison, and (iv) a labeling function that classifies the result of this comparison. Despite the adoption of a SQL-like syntax that hides the complexity of the assessment process, writing a complete assess statement is not easy. In this paper we focus on making the user experience more comfortable by letting the system suggest suitable completions for partially-specified statements. To this end we propose two interaction modes: progressive refinement and auto-completion, both starting from an assess statement partially declared by the user. These two modes are evaluated both in terms of scalability and user experience, with the support of two experiments made with real users.

Trajectory data has become ubiquitous nowadays, which can benefit various real-world applications such as traffic management and location-based services. However, trajectories may disclose highly sensitive information of an individual including mobility patterns, personal profiles and gazetteers, social relationships, etc, making it indispensable to consider privacy protection when releasing trajectory data. Ensuring privacy on trajectories demands more than hiding single locations, since trajectories are intrinsically sparse and high-dimensional, and require to protect multi-scale correlations. To this end, extensive research has been conducted to design effective techniques for privacy-preserving trajectory data publishing. Furthermore, protecting privacy requires carefully balance two metrics: privacy and utility. In other words, it needs to protect as much privacy as possible and meanwhile guarantee the usefulness of the released trajectories for data analysis. In this survey, we provide a comprehensive study and a systematic summarization of existing protection models, privacy and utility metrics for trajectories developed in the literature. We also conduct extensive experiments on two real-life public trajectory datasets to evaluate the performance of several representative privacy protection models, demonstrate the trade-off between privacy and utility, and guide the choice of the right privacy model for trajectory publishing given certain privacy and utility desiderata.

Trajectory-based spatiotemporal entity linking is to match the same moving object in different datasets based on their movement traces. It is a fundamental step to support spatiotemporal data integration and analysis. In this paper, we study the problem of spatiotemporal entity linking using effective and concise signatures extracted from their trajectories. This linking problem is formalized as a$k$-nearest neighbor ($k$-NN) query on the signatures. Four representation strategies (sequential, temporal, spatial, and spatiotemporal) and two quantitative criteria (commonality and unicity) are investigated for signature construction. A simple yet effective dimension reduction strategy is developed together with a novel indexing structure called the WR-tree to speed up the search. A number of optimization methods are proposed to improve the accuracy and robustness of the linking. Our extensive experiments on real-world datasets verify the superiority of our approach over the state-of-the-art solutions in terms of both accuracy and efficiency.

Assessment is the process of comparing the actual to the expected behavior of a business phenomenon and judging the outcome of the comparison. In this paper we propose assess, a novel querying operator that supports assessment based on the results of a query on a data cube. This operator requires (1) the specification of an OLAP query over a measure of a data cube, to define the target cube to be assessed; (2) the specification of a reference cube of comparison (benchmark), which represents the expected performance of the measure; (3) the specification of how to perform the comparison between the target cube and the benchmark, and (4) a labeling function that classifies the result of this comparison using a set of labels. After introducing an SQL-like syntax for our operator, we formally define its semantics in terms of a set of logical operators. To support the computation of assess we propose a basic plan as well as some optimization strategies, then we experimentally evaluate their performance using a prototype.

The democratization of data access and the adoption of OLAP in scenarios requiring hand-free interfaces push towards the creation of smart OLAP interfaces. In this demonstration we present COOL, a tool supporting natural language COnversational OLap sessions. COOL interprets and translates a natural language dialogue into an OLAP session that starts with a GPSJ (Generalized Projection, Selection and Join) query. The interpretation relies on a formal grammar and a knowledge base storing metadata from a multidimensional cube. COOL is portable, robust, and requires minimal user intervention. It adopts an n-gram based model and a string similarity function to match known entities in the natural language description. In case of incomplete text description, COOL can obtain the correct query either through automatic inference or through interactions with the user to disambiguate the text. The goal of the demonstration is to let the audience evaluate the usability of COOL and its capabilities in assisting query formulation and ambiguity/error resolution.

Social BI (SBI) is the emerging discipline that aims at combining corporate data with textual user-generated content (UGC) to let decision-makers analyze their business based on the trends perceived from the environment. Despite the increasing diffusion of SBI applications, no specific and organic design methodology is available yet. In this paper we propose an iterative methodology for designing and maintaining SBI applications that reorganizes the activities and tasks normally carried out by practitioners. Effective support to quick maintenance iterations is a key feature in this context due to the huge dynamism of the UGC and to the pressing need of immediately perceiving and timely reacting to changes in the environment. The paper is completed by two case studies of real SBI projects, related to Italian politics and to the consumer goods area respectively, aimed at proving that the adoption of a structured methodology positively impacts on the project success.