Collaborative OLAP with Tag Clouds: Web 2.0 OLAP Formalism and Experimental Evaluation

Increasingly, business projects are ephemeral. New Business Intelligence tools must support ad-lib data sources and quick perusal. Meanwhile, tag clouds are a popular community-driven visualization technique. Hence, we investigate tag-cloud views with support for OLAP operations such as roll-ups, slices, dices, clustering, and drill-downs. As a case study, we implemented an application where users can upload data and immediately navigate through its ad hoc dimensions. To support social networking, views can be easily shared and embedded in other Web sites. Algorithmically, our tag-cloud views are approximate range top-k queries over spontaneous data cubes. We present experimental evidence that iceberg cuboids provide adequate online approximations. We benchmark several browser-oblivious tag-cloud layout optimizations.Comment: Software at https://github.com/lemire/OLAPTagClou

Doctor of Philosophy

dissertationRecent advancements in mobile devices - such as Global Positioning System (GPS), cellular phones, car navigation system, and radio-frequency identification (RFID) - have greatly influenced the nature and volume of data about individual-based movement in space and time. Due to the prevalence of mobile devices, vast amounts of mobile objects data are being produced and stored in databases, overwhelming the capacity of traditional spatial analytical methods. There is a growing need for discovering unexpected patterns, trends, and relationships that are hidden in the massive mobile objects data. Geographic visualization (GVis) and knowledge discovery in databases (KDD) are two major research fields that are associated with knowledge discovery and construction. Their major research challenges are the integration of GVis and KDD, enhancing the ability to handle large volume mobile objects data, and high interactivity between the computer and users of GVis and KDD tools. This dissertation proposes a visualization toolkit to enable highly interactive visual data exploration for mobile objects datasets. Vector algebraic representation and online analytical processing (OLAP) are utilized for managing and querying the mobile object data to accomplish high interactivity of the visualization tool. In addition, reconstructing trajectories at user-defined levels of temporal granularity with time aggregation methods allows exploration of the individual objects at different levels of movement generality. At a given level of generality, individual paths can be combined into synthetic summary paths based on three similarity measures, namely, locational similarity, directional similarity, and geometric similarity functions. A visualization toolkit based on the space-time cube concept exploits these functionalities to create a user-interactive environment for exploring mobile objects data. Furthermore, the characteristics of visualized trajectories are exported to be utilized for data mining, which leads to the integration of GVis and KDD. Case studies using three movement datasets (personal travel data survey in Lexington, Kentucky, wild chicken movement data in Thailand, and self-tracking data in Utah) demonstrate the potential of the system to extract meaningful patterns from the otherwise difficult to comprehend collections of space-time trajectories

Collaborative OLAP with Tag Clouds: Web 2.0 OLAP Formalism and Experimental Evaluation

Increasingly, business projects are ephemeral. New Business Intelligence tools must support ad-lib data sources and quick perusal. Meanwhile, tag clouds are a popular community-driven visualization technique. Hence, we investigate tag-cloud views with support for OLAP operations such as roll-ups, slices, dices, clustering, and drill-downs. As a case study, we implemented an application where users can upload data and immediately navigate through its ad hoc dimensions. To support social networking, views can be easily shared and embedded in other Web sites. Algorithmically, our tag-cloud views are approximate range top-k queries over spontaneous data cubes. We present experimental evidence that iceberg cuboids provide adequate online approximations. We benchmark several browser-oblivious tag-cloud layout optimizations

Collaborative OLAP with Tag Clouds: Web 2.0 OLAP Formalism and Experimental Evaluation

Increasingly, business projects are ephemeral. New Business Intelligence tools must support ad-lib data sources and quick perusal. Meanwhile, tag clouds are a popular community-driven visualization technique. Hence, we investigate tag-cloud views with support for OLAP operations such as roll-ups, slices, dices, clustering, and drill-downs. As a case study, we implemented an application where users can upload data and immediately navigate through its ad hoc dimensions. To support social networking, views can be easily shared and embedded in other Web sites. Algorithmically, our tag-cloud views are approximate range top-k queries over spontaneous data cubes. We present experimental evidence that iceberg cuboids provide adequate online approximations. We benchmark several browser-oblivious tag-cloud layout optimizations

ICT tools for data management and analysis to support decisional process oriented to sustainable agri-food chains

Il settore agroalimentare sta affrontando delle sfide globali. La prima riguarda sfamare la popolazione mondiale che nel 2050, secondo le proiezioni delle Nazioni Unite, raggiungerà quota 9,3 miliardi di persone. La seconda sfida riguarda la richiesta da parte dei consumatori di prodotti ottenuti da filiere agroalimentari sempre più sostenibili, sicure e trasparenti. In particolare, l’Agricoltura sostenibile è una tecnica di gestione in grado di preservare la diversità biologica, la produttività, la capacità di rigenerazione, la vitalità e l’abilità alla funzione di un ecosistema agricolo, assicurandone, oggi e in futuro, le funzioni ecologiche, economiche e sociali a livello locale, nazionale ed globale, senza danneggiare altri ecosistemi. Quindi, per fronteggiare la sfida dell’agricoltura sostenibile, gli agricoltori devono aumentare la qualità e la quantità della produzione, riducendo l’impatto ambientale attraverso nuovi strumenti e nuove strategie di gestione. Questo lavoro analizza l’integrazione nel settore agroalimentare di alcune tecnologie e metodologie ICT per l’acquisizione, gestione e analisi dei dati, come la tecnologia RFID (Radio Frequency IDentification), i FMIS (Farm Management Information Systems), i DW (Data Warehouse) e l’approccio OLAP (On-Line Analytical Processing). Infine, l’adozione delle tecnologie ICT da parte di vere aziende è stata valutata attraverso un questionario. Al riguardo dell’adozione delle tecnologie RFID, questo lavoro analizza l’opportunità di trasferimento tecnologico relativo al monitoraggio e controllo dei prodotti agroalimentari tramite l’utilizzo di sensori innovativi, intelligenti e miniaturizzati. Le informazioni riguardanti lo stato del prodotto sono trasferite in tempo reale in wireless, come previsto dalla tecnologia RFID. In particolare, due soluzioni RFID sono state analizzate, evidenziando vantaggi e punti critici in confronto ai classici sistemi per assicurare la tracciabilità e la qualità dei prodotti agroalimentari. Quindi, questo lavoro analizza la possibilità di sviluppare una struttura che combina le tecnologie della Business Intelligence con i principi della Protezione Integrata (IPM) per aiutare gli agricoltori nel processo decisionale, andando a diminuire l’impatto ambientale ed aumentare la performance produttiva. L’IPM richiede di utilizzare simultaneamente diverse tecniche di protezione delle colture per il controllo dei parassiti e patogeni tramite un approccio ecologico ed economico. Il sistema di BI proposto è chiamato BI4IPM e combina l’approccio OLTP (On-Line Transaction Processing) con quello OLAP per verificare il rispetto dei disciplinari di produzione integrata. BI4IPM è stato testato con dati provenienti da vere aziende olivicole pugliesi. L’olivo è una delle principali colture a livello globale e la Puglia è la prima regione produttrice in Italia, con un gran numero di aziende che generano dati sull’IPM. Le strategie di protezione delle colture sono correlate alle condizioni climatiche, considerando la forte relazione tra clima, colture e parassiti. Quindi, in questo lavoro è presentato un nuovo e avanzato modello OLAP che integra il GSI (Growing Season Index), un modello fenologico, per comparare indirettamente le aziende agricole dal punto di vista climatico. Il sistema proposto permette di analizzare dati IPM di diverse aziende agricole che presentano le stesse condizioni fenologiche in un anno al fine di individuare best practices e di evidenziare e spiegare pratiche differenti adottate da aziende che lavorano in differenti condizioni climatiche. Infine, è stata effettuata un’indagine al fine di capire come le aziende agricole della Basilicata si raggruppano in funzione del livello di innovazione adottato. È stato utilizzato un questionario per domandare alle aziende se adottano strumenti ICT, ed eventualmente in quale processo produttivo o di management vengano usati. È stata quindi effettuata un’analisi cluster sui dati raccolti. I risultati mostrano che, usando il metodo di clustering k-means, appaiono due gruppi: gli innovatori e gli altri. Mentre, applicando la rappresentazione boxlot, si ottengono 3 gruppi: innovatori, utilizzatori precoci e ritardatari.The Agri-Food sector is facing global challenges. The first issue concerns feeding a world population that in 2050, according to United Nations projections, will reach 9.3 billion people. The second challenge is the request by consumers for high quality products obtained by more sustainable, safely and clear agri-food chains. In particular, the Sustainable agriculture is a management strategy able to preserve the biological diversity, productivity, regeneration capacity, vitality and ability to function of an agricultural ecosystem, ensuring, today and in the future, significant ecological, economic and social functions at the local, national and global scales, without harming other ecosystems. Therefore, to face the challenge of the sustainable agriculture, farmers need to increase quality and quantity of the production, reducing the environmental impact through new management strategies and tools. This work explores the integration of several ICT technologies and methodologies in the agri-food sector for the data acquisition, management and analysis, such as RFID technology, Farm Management Information Systems (FMIS), Data Warehouse (DW) and On-Line Analytical Processing (OLAP). Finally, the adoption of the ICT technologies by real farms is evaluated through a survey. Regarding the adoption of the RFID technology, this work explores an opportunity for technology transfer related to the monitoring and control of agri-food products, based on the use of miniaturized, smart and innovative sensors. The information concerning to the state of the product is transferred in real time in a wireless way, according to the RFID technology. In particular, two technical solutions involving RFID are provided, highlighting the advantages and critical points referred to the normal system used to ensure the traceability and the quality of the agri-food products. Therefore, this work explores the possibility of developing a framework that combines business intelligence (BI) technologies with Integrated Pest Management (IPM) principles to support farmers in the decisional process, thereby decreasing environmental cost and improving production performance. The IPM requires the simultaneous use of different crop protection techniques to control pests through an ecological and economic approach. The proposed BI system is called BI4IPM, and it combines on-line transaction processing (OLTP) with OLAP to verify adherence to the IPM technical specifications. BI4IPM is tested with data from real Apulian olive crop farms. Olive tree is one of the most important crop at global scale and Apulia is the first olive-producing region in Italy, with a huge amount of farms that generate IPM data. The crop protection strategies are correlated to the climate conditions considering the very important relation among climate, crops and pests. Therefore, in this work is presented a new advanced OLAP model integrating the Growing Season Index (GSI), a phenology model, to compare indirectly the farms by a climatic point of view. The proposed system allows analysing IPM data of different farms having the same phenological conditions over a year to understand some best practices and to highlight and explain different practices adopted by farms working in different climatic conditions. Finally, a survey aimed at investigating how Lucania' farms cluster according to the level of innovation adopted was performed. It was used a questionnaire for asking if farms adopt ICTs tools and, in case, what type they involved in managing and/or production processes. It has been done a cluster analysis on collected data. Results show that, using k-means clustering method, appear two clusters: innovators, remaining groups. While, using boxplot representation, clustered three groups: innovators, early adopters and laggards

Technical report of data mining

No abstract availabl

A conceptual framework and a risk management approach for interoperability between geospatial datacubes

De nos jours, nous observons un intérêt grandissant pour les bases de données géospatiales multidimensionnelles. Ces bases de données sont développées pour faciliter la prise de décisions stratégiques des organisations, et plus spécifiquement lorsqu’il s’agit de données de différentes époques et de différents niveaux de granularité. Cependant, les utilisateurs peuvent avoir besoin d’utiliser plusieurs bases de données géospatiales multidimensionnelles. Ces bases de données peuvent être sémantiquement hétérogènes et caractérisées par différent degrés de pertinence par rapport au contexte d’utilisation. Résoudre les problèmes sémantiques liés à l’hétérogénéité et à la différence de pertinence d’une manière transparente aux utilisateurs a été l’objectif principal de l’interopérabilité au cours des quinze dernières années. Dans ce contexte, différentes solutions ont été proposées pour traiter l’interopérabilité. Cependant, ces solutions ont adopté une approche non systématique. De plus, aucune solution pour résoudre des problèmes sémantiques spécifiques liés à l’interopérabilité entre les bases de données géospatiales multidimensionnelles n’a été trouvée. Dans cette thèse, nous supposons qu’il est possible de définir une approche qui traite ces problèmes sémantiques pour assurer l’interopérabilité entre les bases de données géospatiales multidimensionnelles. Ainsi, nous définissons tout d’abord l’interopérabilité entre ces bases de données. Ensuite, nous définissons et classifions les problèmes d’hétérogénéité sémantique qui peuvent se produire au cours d’une telle interopérabilité de différentes bases de données géospatiales multidimensionnelles. Afin de résoudre ces problèmes d’hétérogénéité sémantique, nous proposons un cadre conceptuel qui se base sur la communication humaine. Dans ce cadre, une communication s’établit entre deux agents système représentant les bases de données géospatiales multidimensionnelles impliquées dans un processus d’interopérabilité. Cette communication vise à échanger de l’information sur le contenu de ces bases. Ensuite, dans l’intention d’aider les agents à prendre des décisions appropriées au cours du processus d’interopérabilité, nous évaluons un ensemble d’indicateurs de la qualité externe (fitness-for-use) des schémas et du contexte de production (ex., les métadonnées). Finalement, nous mettons en œuvre l’approche afin de montrer sa faisabilité.Today, we observe wide use of geospatial databases that are implemented in many forms (e.g., transactional centralized systems, distributed databases, multidimensional datacubes). Among those possibilities, the multidimensional datacube is more appropriate to support interactive analysis and to guide the organization’s strategic decisions, especially when different epochs and levels of information granularity are involved. However, one may need to use several geospatial multidimensional datacubes which may be semantically heterogeneous and having different degrees of appropriateness to the context of use. Overcoming the semantic problems related to the semantic heterogeneity and to the difference in the appropriateness to the context of use in a manner that is transparent to users has been the principal aim of interoperability for the last fifteen years. However, in spite of successful initiatives, today's solutions have evolved in a non systematic way. Moreover, no solution has been found to address specific semantic problems related to interoperability between geospatial datacubes. In this thesis, we suppose that it is possible to define an approach that addresses these semantic problems to support interoperability between geospatial datacubes. For that, we first describe interoperability between geospatial datacubes. Then, we define and categorize the semantic heterogeneity problems that may occur during the interoperability process of different geospatial datacubes. In order to resolve semantic heterogeneity between geospatial datacubes, we propose a conceptual framework that is essentially based on human communication. In this framework, software agents representing geospatial datacubes involved in the interoperability process communicate together. Such communication aims at exchanging information about the content of geospatial datacubes. Then, in order to help agents to make appropriate decisions during the interoperability process, we evaluate a set of indicators of the external quality (fitness-for-use) of geospatial datacube schemas and of production context (e.g., metadata). Finally, we implement the proposed approach to show its feasibility

Semantics-Space-Time Cube. A Conceptual Framework for Systematic Analysis of Texts in Space and Time

We propose an approach to analyzing data in which texts are associated with spatial and temporal references with the aim to understand how the text semantics vary over space and time. To represent the semantics, we apply probabilistic topic modeling. After extracting a set of topics and representing the texts by vectors of topic weights, we aggregate the data into a data cube with the dimensions corresponding to the set of topics, the set of spatial locations (e.g., regions), and the time divided into suitable intervals according to the scale of the planned analysis. Each cube cell corresponds to a combination (topic, location, time interval) and contains aggregate measures characterizing the subset of the texts concerning this topic and having the spatial and temporal references within these location and interval. Based on this structure, we systematically describe the space of analysis tasks on exploring the interrelationships among the three heterogeneous information facets, semantics, space, and time. We introduce the operations of projecting and slicing the cube, which are used to decompose complex tasks into simpler subtasks. We then present a design of a visual analytics system intended to support these subtasks. To reduce the complexity of the user interface, we apply the principles of structural, visual, and operational uniformity while respecting the specific properties of each facet. The aggregated data are represented in three parallel views corresponding to the three facets and providing different complementary perspectives on the data. The views have similar look-and-feel to the extent allowed by the facet specifics. Uniform interactive operations applicable to any view support establishing links between the facets. The uniformity principle is also applied in supporting the projecting and slicing operations on the data cube. We evaluate the feasibility and utility of the approach by applying it in two analysis scenarios using geolocated social media data for studying people's reactions to social and natural events of different spatial and temporal scales