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.

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.

Abstract Multi-model DBMSs, which support different data models with a fully integrated backend, have been shown to be beneficial to data warehouses and OLAP systems. Indeed, they can store data according to the multidimensional model and, at the same time, let each of its elements be represented through the most appropriate model. An open challenge in this context is the lack of methods for logical design. Indeed, in a multi-model context, several alternatives emerge for the logical representation of dimensions and facts. The goal of this paper is to devise a set of guidelines for the logical design of multi-model data warehouses so that the designer can achieve the best trade-off between features such as querying, storage, and ETL. To this end, for each model considered (relational, document-based, and graph-based) and for each type of multidimensional element (e.g., non-strict hierarchy) we propose some solutions and carry out a set of intra-model and inter-model comparisons. The resulting guidelines are then tested on a case study that shows all types of multidimensional elements.

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.

International audience

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.

A key role in OLAP analyses of textual user-generated con-tent for social business intelligence (SBI) is played by topics, i.e., concepts of interest within a subject area. Topic hier-archies are irregular, heterogeneous, dynamic, and possibly schemaless; besides, unlike in traditional OLAP, di↵erent semantics for topic aggregation can be envisioned. In this demonstration we present an architecture for SBI based on meta-stars, a novel approach to topic modeling in ROLAP systems. By coupling meta-modeling with navigation tables, meta-stars can cope with changes in the schema of irregular hierarchies and with schemaless ones; besides, they enable a new class of OLAP queries based on semantically-aware aggregation. The demonstration will focus both on the hier-archy update process and on the querying expressiveness. 1.

The emerging medical models aim at leveraging on high-throughput genome sequencing technologies to better target drugs to patients' personal profiles so as to increase their effectiveness. However, the huge amount of data made available by these technologies calls for sophisticated and automated analysis techniques. In this direction we present GOLAM, a framework for OLAP analysis and mining of matches between genomic regions extracted from ENCODE, a worldwide-available collection of shared genomic data. The goal of GOLAM is to overcome the current limitations of genome analysis methods, that are normally based on browsing. This is done by partially automating and speeding-up the analysis process on the one hand, by making it more flexible and introducing a multi-resolution view of data on the other. The framework has been partially implemented so far; in this paper we focus on conveying its potential and on describing its functional architecture and the underlying data models.

Location intelligence is a set of tools and techniques to integrate spatial features into BI platforms, aimed at better monitoring and interpreting business events related to the territory. In this demonstration we present Lily, a geo-enhanced library that relies on a spatial data warehouse to add real location intelligence capabilities to existing BI platforms. Lily provides end-users with a highly-interactive interface that seamlessly achieves a bidirectional integration between the BI and the geospatial worlds, so as to enable advanced analytical, prediction, and simulation features taking into account the spatial dimension. In particular we focus on the impact of Lily on the user experience with reference to three case studies in the domain of healthcare, telco, and school services respectively.

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.

The approaches to data warehouse design are based on the assumption that source data are known in advance and available. While this assumption is true in common project situations, in some peculiar contexts it is not. This is the case of the French national project for analysis of energetic agricultural farms, that is the case study of this paper. Here, the above-mentioned methods can hardly be applied because source data can only be identified and collected once user requirements indicate a need. Besides, the users involved in this project found it very hard to express their analysis needs in abstract terms, i.e., without visualizing sample results of queries, which in turn would require availability of source data. To solve this deadlock we propose ProtOLAP, a tool-assisted fast prototyping methodology that enables quick and reliable test and validation of data warehouse schemata in situations where data supply is collected on users' demand and users' ICT skills are minimal. To this end, users manually feed sample realistic data into a prototype created by designers, then they access and explore these sample data using pivot tables to validate the prototype.

Social business intelligence is the discipline of combining corporate data with user-generated content (UGC) to let decision-makers improve their business based on the trends perceived from the environment. A key role in the analysis of textual UGC is played by topics, meant as specific concepts of interest within a subject area. To enable aggregations of topics at different levels, a topic hierarchy is to be defined. Some attempts have been made to address some of the peculiarities of topic hierarchies, but no comprehensive solution has been found so far. The approach we propose to model topic hierarchies in ROLAP systems is called meta-stars. Its basic idea is to use meta-modeling coupled with navigation tables and with traditional dimension tables: navigation tables support hierarchy instances with different lengths and with non-leaf facts, and allow different roll-up semantics to be explicitly annotated; meta-modeling enables hierarchy heterogeneity and dynamics to be accommodated; dimension tables are easily integrated with standard business hierarchies. After outlining a reference architecture for social business intelligence and describing the meta-star approach, we discuss its effectiveness and efficiency by showing its querying expressiveness and by presenting some experimental results for query performances.

One of the problems in analyzing large multidimensional databases through OLAP sessions is that decision makers can be overwhelmed by the size of query answers, while they need a concise summary of data. Intensional query answering can help by providing a concise description of extensional answers (i.e., the sets of retrieved facts), generally relying on knowledge like integrity constraints, taxonomies, or patterns discovered from data. This paper proposes a framework for computing an intensional answer to an OLAP query by leveraging on the previous queries in the current session. Such intensional answer is concise and semantically rich, and allows the size of the extensional answers returned to be reduced, so as to achieve an effective trade-off between conciseness and informational content. After describing the general framework, we propose a specific instantiation that relies on previous contributions in cube modeling and intensional query answering.

In this demonstration we present MYOLAP, a Java-based tool that allows OLAP analyses to be personalized and enhanced by expressing “soft” query constraints in the form of user preferences. MYOLAP is based on a novel preference algebra and a preference evaluation algorithm specifically devised for the OLAP domain. Preferences are formulated either visually or through an extension of the MDX language, and user interaction with the results is mediated by a visual graph-like structure that shows better-than relationships between different sets of data. The demonstration will show how analysis sessions can benefit from coupling ad-hoc preference constructors with the classical OLAP operators, and in particular how MYOLAP supports users in expressing preference queries, analyzing their results, and navigating datacubes.

Multidimensional databases are the core of business intelligence systems. Their users express complex OLAP queries, often returning large volumes of facts, sometimes providing little or no information. Thus, expressing preferences could be highly valuable in this domain. The OLAP domain is representative of an unexplored class of preference queries, characterized by three peculiarities: preferences can be expressed on both numerical and categorical domains; they can also be expressed on the aggregation level of facts; the space on which preferences are expressed includes both elemental and aggregated facts. In this paper, we present myOLAP, an approach for expressing and evaluating OLAP preferences, devised by taking into account the three peculiarities above. We first propose a preference algebra where users are enabled to express their preferences, besides on attributes and measures, also on the aggregation level of facts, for instance, by stating that monthly data are preferred to yearly and daily data. Then, with respect to preference evaluation, we propose an algorithm called WeSt that relies on a novel graph representation where two types of domination between sets of facts may be expressed, which considerably improves efficiency. The approach is extensively tested for efficiency and effectiveness on real data, and compared against two other approaches in the literature.

Inter-business collaborative contexts prefigure a distributed scenario where companies organize and coordinate themselves to develop common and shared opportunities. Traditional business intelligence systems do not provide support to this end. Peer Data Management Systems (PDMSs) have been proposed as architectures to support sharing of operational data across networks of peers while guaranteeing peers' autonomy, based on semantic mappings that mediate between the heterogeneous schemata exposed by peers. In line with the PDMS infrastructure, 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 be properly reformulated on the other peers. In this direction, 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 these mappings towards relational schemata. Finally, we sketch the query reformulation algorithm by outlining the reformulation steps of typical OLAP queries.

Testing is an essential part of the design life-cycle of any software product. Nevertheless, while most phases of data warehouse design have received considerable attention in the literature, not much has been said about data warehouse testing. In this paper we introduce a number of data mart-specific testing activities, we classify them in terms of what is tested and how it is tested, and we discuss how they can be framed within a reference design methodology.

Expressing preferences when querying databases is a natural way to avoid empty results and information flooding, and in general to rank results so that the user may first see the data that better match his tastes. In this paper we outline the main research issues to be faced in order to develop a system for handling user preferences on OLAP cubes.

In this demo we present X-Time, a prototype for managing schema versioning in relational data warehouses, specifically oriented to support the formulation of cross-version queries, i.e., queries whose temporal horizon spans multiple versions. The key issue to increase querying flexibility is the introduction of augmented schemata that properly extend previous schema versions.

In order to be able to evaluate beforehand the impact of a strategical or tactical move, decision makers need reliable previsional systems. What-ifanalysis satisifies this need by enabling users to simulate and inspect the behavior of a complex system under some given hypotheses, called scenarios. Though a few commercial tools are capable of performing forecasting and what-if analysis, and some papers describe relevant applications in different fields, no attempt has been made so far to comprehensively address methodological and modeling issues in this field. This paper is a preliminary work in the direction of devising a structured approach to designing what-if applications in the BI context. Its goal is to summarize the main lessons we have learnt by facing real what-if projects, and to discuss the related research issues. We also provide a methodological framework for design and discuss its application to a case study.

Multidimensional modeling requires specialized design techniques. Though a lot has been written about how a data warehouse should be designed, there is no consensus on a design method yet. This paper follows from a wide discussion that took place in Dagstuhl, during the Perspectives Workshop "Data Warehousing at the Crossroads", and is aimed at outlining some open issues in modeling and design of data warehouses. More precisely, issues regarding conceptual models, logical models, methods for design, interoperability, and design for new architectures and applications are considered.

Several surveys indicate that a significant percentage of data warehouses fail to meet business objectives or are outright failures. One of the reasons for this is that requirement analysis is typically overlooked in real projects. In this paper we propose a goal-oriented approach to requirement analysis for data warehouses, based on the Tropos methodology. Two different perspectives are integrated for requirement analysis: organizational modeling, centered on stakeholders, and decisional modeling, focused on decision makers. Our approach can be employed within both a demand-driven and a mixed supply/demand-driven design framework: in the second case, while the operational sources are still explored to shape hierarchies, user requirements play a fundamental role in restricting the area of interest for analysis and in choosing facts, dimensions, and measures. The methodology proposed, supported by a prototype, is described with reference to a real case study.

During the last ten years the approach to business management has deeply changed, and companies have understood the importance of enforcing achievement of the goals defined by their strategy through metrics-driven management. The DW process, though supporting bottom-up extraction of information from data, fails in top-down enforcing the company strategy. A new approach to BI, called Business Performance Management (BPM), is emerging from this framework: it includes DW but it also requires a reactive component capable of monitoring the time-critical operational processes to allow tactical and operational decision-makers to tune their actions according to the company strategy. The aim of this paper is to encourage the research community to acknowledge the coming of a second era in BI, to propose a general architecture for BPM, and to lay the premises for investigating the most challenging of the related issues.

A large amount of data needed in decision-making processes is stored in the XML data format, which is widely used for e-commerce and Internet-based information exchange. Thus, as more organizations view the web as an integral part of their communication and business, the importance of integrating XML data in data warehousing environments is becoming increasingly high. In this paper we show how the design of a data mart can be carried out starting directly from an XML source. Two main issues arise: on the one hand, since XML models semi-structured data, not all the information needed for design can be safely derived; on the other, different approaches for representing relationships in XML DTDs and Schemas are possible, each with different expressive power. After discussing these issues, we propose a semi-automatic approach for building the conceptual schema for a data mart starting from the XML sources.

A multidimensional database can be seen as a collection of multidimensional cubes, from which information is usually extracted by aggregation; aggregated data can be calculated either from cubes containing elemental data or from views in which partially aggregated data are stored. Thus, view materialization and run-time optimization through query rewriting become crucial issues in determining the overall performance. The capability of matching two queries i s necessary to address both issues; unfortunately, most works in this field consider only simple categories of queries. In this paper we focus on a relevant class of queries, those modeled by Nested Generalized Projection / Selection / Join (NGPSJ) expressions, in which different aggregation functions may be applied in sequence to the same measure and selections may be formulated, at different granularities, on both dimensions and measures of the cube. Given two NGPSJ expressions, we show how to recursively compute their ancestor, i.e., the coarsest expression on which both can be rewritten. The ancestor has a key role in view materialization, since it may be used to determine a restricted set of candidate views; given the ancestor, determining if one expression can be rewritten on the other is straightforward. 1.

Though designing a data warehouse requires techniques completely different from those adopted for operational systems, no significant effort has been made so far to develop a complete and consistent design methodology for data warehouses.In this paper we outline a general methodological framework for data warehouse design, based on our Dimensional Fact Model (DFM).After analyzing the existing information system and collecting the user requirements, conceptual design is carried out semi-automatically starting from the operational database scheme.A workload is then characterized in terms of data volumes and expected queries, to be used as the input of the logical and physical design phases whose output is the final scheme for the data warehouse.

Data warehousing systems enable enterprise managers to acquire and integrate information from heterogeneous sources and to query very large databases efficiently. Building a data warehouse requires adopting design and implementation techniques completely different from those underlying operational information systems. Though most scientific literature on the design of data warehouses concerns their logical and physical models, an accurate conceptual design is the necessary foundations for building a DW which is well-documented and fully satisfies requirements. In this paper we formalize a graphical conceptual model for data warehouses, called Dimensional Fact model, and propose a semi-automated methodology to build it from the pre-existing (conceptual or logical) schemes describing the enterprise relational database. The representation of reality built using our conceptual model consists of a set of fact schemes whose basic elements are facts, measures, attributes, dimensions and hierarchies; other features which may be represented on fact schemes are the additivity of fact attributes along dimensions, the optionality of dimension attributes and the existence of non-dimension attributes. Compatible fact schemes may be overlapped in order to relate and compare data for drill-across queries. Fact schemes should be integrated with information of the conjectured workload, to be used as the input of logical and physical design phases; to this end, we propose a simple language to denote data warehouse queries in terms of sets of fact instances.

Exploration is a central issue for autonomous agents which must carry out navigation tasks in environments of which a description is not known a priori. In our approach the environment is described, from a symbolic point of view, by means of a graph; clustering techniques allow for further levels of abstraction to be defined, leading to a multi-layered representation. In this work we propose an unsupervised exploration algorithm in which several agents cooperate to acquire knowledge of the environment at the different abstraction levels. All agents are equal and pursue the same local exploration strategy; nevertheless, the existence of multiple levels of abstraction in the environment representation allows for the agents' behavior to differ. Agents carry out exploration at different abstraction levels, aimed at reproducing an ideal exploration profile; each agent dynamically selects its exploration level, based on the current demand. Inter-agent communication allows for the agents to share their knowledge and to record acquaintances of the other agents. A communication protocol for organizing teams of agents is provided.