Keyword Search in Relational Databases: Architecture, Approaches and Considerations

Questo lavoro di tesi presenta le diverse soluzioni proposte in letteratura per applicare il paradigma keyword search alle basi di dati relazionali, e vuole delineare una architettura generale per definire e sviluppare questi sistemi. A tal proposito, le soluzioni presentate dalla comunità scientifica sono state analizzate focalizzandosi sui singoli componenti della pipeline di ricerca. Infine, si sono analizzati i processi di valutazione sperimentale di questi sistem

Supporting Story Synthesis: Bridging the Gap between Visual Analytics and Storytelling

Visual analytics usually deals with complex data and uses sophisticated algorithmic, visual, and interactive techniques. Findings of the analysis often need to be communicated to an audience that lacks visual analytics expertise. This requires analysis outcomes to be presented in simpler ways than that are typically used in visual analytics systems. However, not only analytical visualizations may be too complex for target audience but also the information that needs to be presented. Hence, there exists a gap on the path from obtaining analysis findings to communicating them, which involves two aspects: information and display complexity. We propose a general framework where data analysis and result presentation are linked by story synthesis, in which the analyst creates and organizes story contents. Differently, from the previous research, where analytic findings are represented by stored display states, we treat findings as data constructs. In story synthesis, findings are selected, assembled, and arranged in views using meaningful layouts that take into account the structure of information and inherent properties of its components. We propose a workflow for applying the proposed framework in designing visual analytics systems and demonstrate the generality of the approach by applying it to two domains, social media, and movement analysis

Analysis of multiple update techniques on a RDF keyword search system

Keyword search is a technology that allows non-expert users to explore and retrieve information and it is traditionally used for unstructured data, such as in Web page searches. In the last decade, this search method has also become popular for exploring structured data, such as relational databases or graphs. Instead of using complex SQL or SPARQL queries and when the underlying schema is known, the user writes a series of words(keywords) to search for what he or she needs, getting as answers the ones more matching with the search. Keyword search systems are challenged by two fundamental parameters, efficiency and effectiveness. In fact, efficiency and effectiveness are two qualities of a SPARQL, or SQL, query that returns an answer quickly and always accurate even when operating on large amounts of data. The "virtual documents" method allows keyword search systems to work also on large databases by generating answers to keyword queries in a reasonable time. This paper aims to replicate the keyword search systems based on "virtual documents" TSA+BM25 and TSA+VDP for RDF graphs. In addition, two methods of update processing in a keyword search system, will be presented and analyzed: BruteForce and semiTSA. Although keyword search is a growing research matter, the topic of updates on structured data, such as RDF data, had not yet been addressed in the literature.Keyword search is a technology that allows non-expert users to explore and retrieve information and it is traditionally used for unstructured data, such as in Web page searches. In the last decade, this search method has also become popular for exploring structured data, such as relational databases or graphs. Instead of using complex SQL or SPARQL queries and when the underlying schema is known, the user writes a series of words(keywords) to search for what he or she needs, getting as answers the ones more matching with the search. Keyword search systems are challenged by two fundamental parameters, efficiency and effectiveness. In fact, efficiency and effectiveness are two qualities of a SPARQL, or SQL, query that returns an answer quickly and always accurate even when operating on large amounts of data. The "virtual documents" method allows keyword search systems to work also on large databases by generating answers to keyword queries in a reasonable time. This paper aims to replicate the keyword search systems based on "virtual documents" TSA+BM25 and TSA+VDP for RDF graphs. In addition, two methods of update processing in a keyword search system, will be presented and analyzed: BruteForce and semiTSA. Although keyword search is a growing research matter, the topic of updates on structured data, such as RDF data, had not yet been addressed in the literature