Generalized Lineage-Aware Temporal Windows: Supporting Outer and Anti Joins in Temporal-Probabilistic Databases

The result of a temporal-probabilistic (TP) join with negation includes, at each time point, the probability with which a tuple of a positive relation ${\bf p}$ matches none of the tuples in a negative relation ${\bf n}$, for a given join condition $\theta$. TP outer and anti joins thus resemble the characteristics of relational outer and anti joins also in the case when there exist time points at which input tuples from ${\bf p}$ have non-zero probabilities to be $true$ and input tuples from ${\bf n}$ have non-zero probabilities to be $false$, respectively. For the computation of TP joins with negation, we introduce generalized lineage-aware temporal windows, a mechanism that binds an output interval to the lineages of all the matching valid tuples of each input relation. We group the windows of two TP relations into three disjoint sets based on the way attributes, lineage expressions and intervals are produced. We compute all windows in an incremental manner, and we show that pipelined computations allow for the direct integration of our approach into PostgreSQL. We thereby alleviate the prevalent redundancies in the interval computations of existing approaches, which is proven by an extensive experimental evaluation with real-world datasets

Spatiotemporal Indexing With the M-Tree

Modern GIS applications for transportation and defense often require the ability to store the evolving positions of a large number of objects as they are observed in motion, and to support queries on this spatiotemporal data in real time. Because the M-Tree has been proven as an index for spatial network databases, we have selected it to be enhanced as a spatiotemporal index. We present modifications to the tree which allow trajectory reconstruction with fast insert performance and modifications which allow the tree to be built with awareness of the spatial locality of reference in spatiotemporal data

Lineage-Aware Temporal Windows: Supporting Set Operations in Temporal-Probabilistic Databases

In temporal-probabilistic (TP) databases, the combination of the temporal and the probabilistic dimension adds significant overhead to the computation of set operations. Although set queries are guaranteed to yield linearly sized output relations, existing solutions exhibit quadratic runtime complexity. They suffer from redundant interval comparisons and additional joins for the formation of lineage expressions. In this paper, we formally define the semantics of set operations in TP databases and study their properties. For their efficient computation, we introduce the lineage-aware temporal window, a mechanism that directly binds intervals with lineage expressions. We suggest the lineage-aware window advancer (LAWA) for producing the windows of two TP relations in linearithmic time, and we implement all TP set operations based on LAWA. By exploiting the flexibility of lineage-aware temporal windows, we perform direct filtering of irrelevant intervals and finalization of output lineage expressions and thus guarantee that no additional computational cost or buffer space is needed. A series of experiments over both synthetic and real-world datasets show that (a) our approach has predictable performance, depending only on the input size and not on the number of time intervals per fact or their overlap, and that (b) it outperforms state-of-the-art approaches in both temporal and probabilistic databases

Supporting Set Operations in Temporal-Probabilistic Databases

In temporal-probabilistic (TP) databases, the combination of the temporal and the probabilistic dimension adds significant overhead to the computation of set operations. Although set queries are guaranteed to yield linearly sized output relations, all of the existing solutions exhibit a quadratic runtime complexity. They suffer from redundant interval comparisons and additional joins for the formation of lineage expressions. In this paper, we formally define TP set operations and study their properties. For their efficient computation, we introduce the lineage-aware temporal window, a mechanism that binds intervals with lineage expressions. We suggest the lineage-aware window advancer (LAWA) for producing lineage-aware temporal windows, which enable direct filtering of irrelevant intervals and finalization of output lineage expressions. This way, we compute TP set operations in linearithmic time. A series of experiments over both synthetic and real-world datasets show that (a) our approach has predictable performance, which depends only on the size of the input relations and not on the number of time intervals per fact or the overlap of the time intervals, and that (b) it outperforms state-of-the-art approaches

A Map-algebra-inspired Approach for Interacting With Wireless Sensor Networks, Cyber-physical Systems or Internet of Things

The typical approach for consuming data from wireless sensor networks (WSN) and Internet of Things (IoT) has been to send data back to central servers for processing and analysis. This thesis develops an alternative strategy for processing and acting on data directly in the environment referred to as Active embedded Map Algebra (AeMA). Active refers to the near real time production of data, and embedded refers to the architecture of distributed embedded sensor nodes. Network macroprogramming, a style of programming adopted for wireless sensor networks and IoT, addresses the challenges of coordinating the behavior of multiple connected devices through a high-level programming model. Several macroprogramming models have been proposed, but none to date has adopted a comprehensive spatial model. This thesis takes the unique approach of adapting the well-known Map Algebra model from Geographic Information Science to extend the functionality of WSN/IoT and the opportunities for user interaction with WSN/IoT. As an inherently spatial model, the Map Algebra-inspired metaphor supports the types of computation desired from a network of geographically dispersed WSN nodes. The AeMA data model aligns with the conceptual model of GIS layers and specific layer operations from Map Algebra. A declarative query and network tasking language, based on Map Algebra operations, provides the basis for operations and interactions. The model adds functionality to calculate and store time series and specific temporal summary-type composite objects as an extension to traditional Map Algebra. The AeMA encodes Map Algebra-inspired operations into an extensible Virtual Machine Runtime system, called MARS (Map Algebra Runtime System) that supports Map Algebra in an efficient and extensible way. Map algebra-like operations are performed in a distributed manner. Data do not leave the network but are analyzed and consumed in place. As a consequence, collected information is available in-situ to drive local actions. The conceptual model and tasking language are designed to direct nodes as active entities, able to perform some actions on their environment. This Map Algebra inspired network macroprogramming model has many potential applications for spatially deployed WSN/IoT networks. In particular the thesis notes its utility for precision agriculture applications

A framework for the management of deformable moving objects

There is an emergence of a growing number of applications and services based on spatiotemporal data in the most diverse areas of knowledge and human activity. The Internet of Things (IoT), the emergence of technologies that make it possible to collect information about the evolution of real world phenomena and the widespread use of devices that can use the Global Positioning System (GPS), such as smartphones and navigation systems, suggest that the volume and value of these data will increase significantly in the future. It is necessary to develop tools capable of extracting knowledge from these data and for this it is necessary to manage them: represent, manipulate, analyze and store, in an efficient way. But this data can be complex, its management is not trivial and there is not yet a complete system capable of performing this task. Works on moving points, that represent the position of objects over time, are frequent in the literature. On the contrary there are much less solutions for the representation of moving regions, that represent the continuous changes in position, shape and extent of objects over time, e.g., storms, fires and icebergs. The representation of the evolution of moving regions is complex and requires the use of more elaborate techniques, e.g., morphing and interpolation techniques, capable of producing realistic and geometrically valid representations. In this dissertation we present and propose a data model for moving objects (moving points and moving regions), in particular for moving regions, based on the concept of mesh and compatible triangulation and rigid interpolation methods. This model was implemented in a framework that is not client or application dependent and we also implemented a spatiotemporal extension for PostgreSQL that uses this framework to manipulate and analyze moving objects, as a proof of concept that our framework works with real applications. The tests’ results using real data, obtained from satellite images of the evolution of 2 icebergs over time, show that our data model works. Besides the results obtained one important contribution of this work is the development of a basic framework for moving objects that can be used as a basis for further investigation in this area. A few problems still remain that must be further studied and analyzed, in particular, the ones that were found when using the compatible triangulation and rigid interpolation methods with real data.Assistimos ao aparecimento de um número crescente de aplicações e serviços baseados em dados espácio-temporais nas mais diversas áreas do conhecimento e da atividade humana. A internet das coisas (IoT), o aparecimento de novas tecnologias que permitem obter dados sobre a evolução de fenómenos do mundo real e o uso generalizado de dispositivos que usam o sistema de posicionamento global (GPS), por exemplo, smartphones e sistemas de navegação, sugerem que o volume e o valor destes dados aumente significativamente no futuro. Torna-se necessário desenvolver ferramentas capazes de extrair conhecimento destes dados e para isso é necessário geri-los: representar, manipular, analisar e armazenar, de uma forma eficiente. Mas estes dados podem ser complexos, a sua gestão não é trivial e ainda não existe um sistema completo capaz de executar essa tarefa. Existe muito trabalho na literatura sobre pontos móveis, que representam as alterações da posição de objectos ao longo do tempo, mas existe muito menos trabalho realizado sobre regiões móveis, que representam as alterações da posição e da forma de regiões ao longo do tempo, por exemplo, uma tempestade, um incêndio ou um derramamento de petroleo. A representação da evolução de regiões móveis ao longo do tempo é complexa e exige o uso de técnicas mais elaboradas, por exemplo, técnicas de morphing e interpolação, capazes de produzir representações realistas e geometricamente válidas. Nesta dissertação apresentamos e propomos um modelo de dados para trabalhar com objetos móveis (pontos móveis e regiões móveis), em particular regiões móveis, baseado no conceito de malha e em métodos de triangulação compatível e interpolação rígida. Este modelo foi implementado num framework que é independente do cliente e da aplicação. Também implementámos uma extensão espácio-temporal para o sistema de gestão de base de dados PostgreSQL, que usa este framework para manipular e analisar objectos móveis, como uma prova de conceito que o nosso framework funciona com aplicações reais. Os resultados dos testes com dados reais, obtidos a partir de imagens de satélite da evolução de 2 icebergs ao longo do tempo, demonstram que o nosso modelo funciona. Para além dos resultados obtidos, um contributo importante desta dissertação é o desenvolvimento de um framework que pode ser usado como a base para trabalho futuro e investigação nesta área. Existem alguns problemas ainda por resolver e que devem ser analisados e estudados com mais cuidado, em particular, os que foram encontrados quando usámos os métodos de triangulação compatível e interpolação rigída em dados reais.Mestrado em Engenharia Informátic

Modeling and querying spatio-temporal clinical databases with multiple granularities

In molti campi di ricerca, i ricercatori hanno la necessit\ue0 di memorizzare, gestire e interrogare dati spazio-temporali. Tali dati sono classici dati alfanumerici arricchiti per\uf2 con una o pi\uf9 componenti temporali, spaziali e spazio-temporali che, con diversi possibili significati, li localizzano nel tempo e/o nello spazio. Ambiti in cui tali dati spazio-temporali devono essere raccolti e gestiti sono, per esempio, la gestione del territorio o delle risorse naturali, l'epidemiologia, l'archeologia e la geografia. Pi\uf9 in dettaglio, per esempio nelle ricerche epidemiologiche, i dati spazio-temporali possono servire a rappresentare diversi aspetti delle malattie e delle loro caratteristiche, quali per esempio la loro origine, espansione ed evoluzione e i fattori di rischio potenzialmente connessi alle malattie e al loro sviluppo. Le componenti spazio-temporali dei dati possono essere considerate come dei "meta-dati" che possono essere sfruttati per introdurre nuovi tipi di analisi sui dati stessi. La gestione di questi "meta-dati" pu\uf2 avvenire all'interno di diversi framework proposti in letteratura. Uno dei concetti proposti a tal fine \ue8 quello delle granularit\ue0. In letteratura c'\ue8 ampio consenso sul concetto di granularit\ue0 temporale, di cui esistono framework basati su diversi approcci. D'altro canto, non esiste invece un consenso generale sulla definizione di un framework completo, come quello delle granularit\ue0 temporali, per le granularit\ue0 spaziali e spazio-temporali. Questa tesi ha lo scopo di riempire questo vuoto proponendo un framework per le granularit\ue0 spaziali e, basandosi su questo e su quello gi\ue0 presente in letteratura per le granularit\ue0 temporali, un framework per le granularit\ue0 spazio-temporali. I framework proposti vogliono essere completi, per questo, oltre alle definizioni dei concetti di granularit\ue0 spaziale e spazio-temporale, includono anche la definizione di diversi concetti legati alle granularit\ue0, quali per esempio le relazioni e le operazioni tra granularit\ue0. Le relazioni permettono di conoscere come granularit\ue0 diverse sono legate tra loro, costruendone anche una gerarchia. Tali informazioni sono poi utili al fine di conoscere se e come \ue8 possibile confrontare dati associati e rappresentati con granularit\ue0 diverse. Le operazioni permettono invece di creare nuove granularit\ue0 a partire da altre granularit\ue0 gi\ue0 definite nel sistema, manipolando o selezionando alcune loro componenti. Basandosi su questi framework, l'obiettivo della tesi si sposta poi sul mostrare come le granularit\ue0 possano essere utilizzate per arricchire basi di dati spazio-temporali gi\ue0 esistenti al fine di una loro migliore e pi\uf9 ricca gestione e interrogazione. A tal fine, proponiamo qui una base di dati per la gestione dei dati riguardanti le granularit\ue0 temporali, spaziali e spazio-temporali. Nella base di dati proposta possono essere rappresentate tutte le componenti di una granularit\ue0 come definito nei framework proposti. La base di dati pu\uf2 poi essere utilizzata per estendere una base di dati spazio-temporale esistente aggiungendo alle tuple di quest'ultima delle referenze alle granularit\ue0 dove quei dati possono essere localizzati nel tempo e/o nel spazio. Per dimostrare come ci\uf2 possa essere fatto, nella tesi introduciamo la base di dati sviluppata ed utilizzata dal Servizio Psichiatrico Territoriale (SPT) di Verona. Tale base di dati memorizza le informazioni su tutti i pazienti venuti in contatto con l'SPT negli ultimi 30 anni e tutte le informazioni sui loro contatti con il servizio stesso (per esempio: chiamate telefoniche, visite a domicilio, ricoveri). Parte di tali informazioni hanno una componente spazio-temporale e possono essere quindi analizzate studiandone trend e pattern nel tempo e nello spazio. Nella tesi quindi estendiamo questa base di dati psichiatrica collegandola a quella proposta per la gestione delle granularit\ue0. A questo punto i dati psichiatrici possono essere interrogati anche sulla base di vincoli spazio-temporali basati su granularit\ue0. L'interrogazione di dati spazio-temporali associati a granularit\ue0 richiede l'utilizzo di un linguaggio d'interrogazione che includa, oltre a strutture, operatori e funzioni spazio-temporali per la gestione delle componenti spazio-temporali dei dati, anche costrutti per l'utilizzo delle granularit\ue0 nelle interrogazioni. Quindi, partendo da un linguaggio d'interrogazione spazio-temporale gi\ue0 presente in letteratura, in questa tesi proponiamo anche un linguaggio d'interrogazione che permetta ad un utente di recuperare dati da una base di dati spazio-temporale anche sulla base di vincoli basati su granularit\ue0. Il linguaggio viene introdotto fornendone la sintassi e la semantica. Inoltre per mostrare l'effettivo ruolo delle granularit\ue0 nell'interrogazione di una base di dati clinica, mostreremo diversi esempi di interrogazioni, scritte con il linguaggio d'interrogazione proposto, sulla base di dati psichiatrica dell'SPT di Verona. Tali interrogazioni spazio-temporali basate su granularit\ue0 possono essere utili ai ricercatori ai fini di analisi epidemiologiche dei dati psichiatrici.In several research fields, temporal, spatial, and spatio-temporal data have to be managed and queried with several purposes. These data are usually composed by classical data enriched with a temporal and/or a spatial qualification. For instance, in epidemiology spatio-temporal data may represent surveillance data, origins of disease and outbreaks, and risk factors. In order to better exploit the time and spatial dimensions, spatio-temporal data could be managed considering their spatio-temporal dimensions as meta-data useful to retrieve information. One way to manage spatio-temporal dimensions is by using spatio-temporal granularities. This dissertation aims to show how this is possible, in particular for epidemiological spatio-temporal data. For this purpose, in this thesis we propose a framework for the definition of spatio-temporal granularities (i.e., partitions of a spatio-temporal dimension) with the aim to improve the management and querying of spatio-temporal data. The framework includes the theoretical definitions of spatial and spatio-temporal granularities (while for temporal granularities we refer to the framework proposed by Bettini et al.) and all related notions useful for their management, e.g., relationships and operations over granularities. Relationships are useful for relating granularities and then knowing how data associated with different granularities can be compared. Operations allow one to create new granularities from already defined ones, manipulating or selecting their components. We show how granularities can be represented in a database and can be used to enrich an existing spatio-temporal database. For this purpose, we conceptually and logically design a relational database for temporal, spatial, and spatio-temporal granularities. The database stores all data about granularities and their related information we defined in the theoretical framework. This database can be used for enriching other spatio-temporal databases with spatio-temporal granularities. We introduce the spatio-temporal psychiatric case register, developed by the Verona Community-based Psychiatric Service (CPS), for storing and managing information about psychiatric patient, their personal information, and their contacts with the CPS occurred in last 30 years. The case register includes both clinical and statistical information about contacts, that are also temporally and spatially qualified. We show how the case register database can be enriched with spatio-temporal granularities both extending its structure and introducing a spatio-temporal query language dealing with spatio-temporal data and spatio-temporal granularities. Thus, we propose a new spatio-temporal query language, by defining its syntax and semantics, that includes ad-hoc features and constructs for dealing with spatio-temporal granularities. Finally, using the proposed query language, we report several examples of spatio-temporal queries on the psychiatric case register showing the ``usage'' of granularities and their role in spatio-temporal queries useful for epidemiological studies