Conversational OLAP in Action

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

Business Intelligence on Non-Conventional Data

The revolution in digital communications witnessed over the last decade had a significant impact on the world of Business Intelligence (BI). In the big data era, the amount and diversity of data that can be collected and analyzed for the decision-making process transcends the restricted and structured set of internal data that BI systems are conventionally limited to. This thesis investigates the unique challenges imposed by three specific categories of non-conventional data: social data, linked data and schemaless data. Social data comprises the user-generated contents published through websites and social media, which can provide a fresh and timely perception about people’s tastes and opinions. In Social BI (SBI), the analysis focuses on topics, meant as specific concepts of interest within the subject area. In this context, this thesis proposes meta-star, an alternative strategy to the traditional star-schema for modeling hierarchies of topics to enable OLAP analyses. The thesis also presents an architectural framework of a real SBI project and a cross-disciplinary benchmark for SBI. Linked data employ the Resource Description Framework (RDF) to provide a public network of interlinked, structured, cross-domain knowledge. In this context, this thesis proposes an interactive and collaborative approach to build aggregation hierarchies from linked data. Schemaless data refers to the storage of data in NoSQL databases that do not force a predefined schema, but let database instances embed their own local schemata. In this context, this thesis proposes an approach to determine the schema profile of a document-based database; the goal is to facilitate users in a schema-on-read analysis process by understanding the rules that drove the usage of the different schemata. A final and complementary contribution of this thesis is an innovative technique in the field of recommendation systems to overcome user disorientation in the analysis of a large and heterogeneous wealth of data

Schema Profiling of Document Stores

In document stores, schema is a soft concept and the documents in a collection can have different schemata; this gives designers and implementers augmented flexibility but requires an extra effort to understand the rules that drove the use of alternative schemata when heterogeneous documents are to be analyzed or integrated. In this paper we outline a technique, called schema profiling, to explain the schema variants within a collection in document stores by capturing the hidden rules explaining the use of these variants; we express these rules in the form of a decision tree, called schema profile, whose main feature is the coexistence of value-based and schema-based conditions. Consistently with the requirements we elicited from real users, we aim at creating explicative, precise, and concise schema profiles; to quantitatively assess these qualities we introduce a novel measure of entropy

Interactive Multidimensional Modeling of Linked Data for Exploratory OLAP

Exploratory OLAP aims at coupling the precision and detail of corporate data with the information wealth of LOD. While some techniques to create, publish, and query RDF cubes are already available, little has been said about how to contextualize these cubes with situational data in an on-demand fashion. In this paper we describe an approach, called iMOLD, that enables non-technical users to enrich an RDF cube with multidimensional knowledge by discovering aggregation hierarchies in LOD. This is done through a user-guided process that recognizes in the LOD the recurring modeling patterns that express roll- up relationships between RDF concepts, then translates these patterns into aggregation hierarchies to enrich the RDF cube. Two families of aggregation patterns are identified, based on associations and generalization respectively, and the algorithms for recognizing them are described. To evaluate iMOLD in terms of efficiency and effectiveness we compare it with a related approach in the literature, we propose a case study based on DBpedia, and we discuss the results of a test made with real users

A data platform for real-time monitoring and analysis of the brown marmorated stink bug in Northern Italy

The brown marmorated stink bug (Halyomorpha halys) is one of the main insect pest species causing economic damage to several agricultural commodities worldwide and one of the worst threats to tree fruit crops in northern Italy, especially in the Emilia-Romagna region. Previous efforts in implementing H. halys surveillance at the regional level were mainly focused on studying the H. halys phenology, but they were not designed to provide a public service. In this paper, we propose a data-driven approach to support the application of Integrated Pest Management strategies against H. halys. The proposal is based on the experience of a three-year project in which a network of monitoring traps has been deployed throughout the whole Emilia-Romagna region and a data platform has been implemented to enable the real-time tracking of H. halys occurrence and distribution, integrating these information with multiple data sources, and analytical capabilities through a public website. Besides the real-time pest surveillance, the data platform allowed us to increase our understanding about H. halys seasonal invasion dynamics and the main factors contributing to its spread. The results will help individual growers in protecting their crops and the whole region in promoting more efficient usage of insecticides and more sustainable and healthy agricultural productions

Interactive multidimensional modeling of linked data for exploratory OLAP

Exploratory OLAP aims at coupling the precision and detail of corporate data with the information wealth of LOD. While some techniques to create, publish, and query RDF cubes are already available, little has been said about how to contextualize these cubes with situational data in an on-demand fashion. In this paper we describe an approach, called iMOLD, that enables non-technical users to enrich an RDF cube with multidimensional knowledge by discovering aggregation hierarchies in LOD. This is done through a user-guided process that recognizes in the LOD the recurring modeling patterns that express roll-up relationships between RDF concepts, then translates these patterns into aggregation hierarchies to enrich the RDF cube. Two families of aggregation patterns are identified, based on associations and generalization respectively, and the algorithms for recognizing them are described. To evaluate iMOLD in terms of efficiency and effectiveness we compare it with a related approach in the literature, we propose a case study based on DBpedia, and we discuss the results of a test made with real users.Peer ReviewedPostprint (author's final draft