International Telemetering Conference Proceedings / November 04-07, 1991 / Riviera Hotel and Convention Center, Las Vegas, Nevada / A synopsis of the very recent evolution of telemetry chart recorders from “closed” chart paper output devices to powerful “open” Data Management Systems. A Data Management System (DMS) is defined as one which incorporates a video screen for waveform preview and monitoring, direct connection of hard or optical disk via SCSI for real-time data archiving, and DR11 digital interfacing. The DMS concept of providing real-time waveform monitoring independent of hard copy recording is discussed, as well as the capabilities of the hard copy recorder. The realities of budget shortfalls makes wholesale system upgrades to eliminate DAC’s entirely difficult at best. These concerns—and a potential remedy: a DMS which accepts any mix of analog and digital waveforms—are reviewed. Objectives: How DMS’s can be integrated with existing telemetry systems, encompass the functionality of conventional recorders and add new capabilities, with an emphasis on how data can be digitally pre-formatted in real-time, simplifying—or even eliminating—post-mission reduction and analysis. A demonstration of how a video display allows real-time trace viewing—a major weakness of conventional thermal array recorders.

The purpose of this thesis is to explore and study the usability issues within Technical Data Management Systems (TDMS). The research has been conducted as a single case study at the gardening and landscape maintenance company Husqvarna. The inductive research led to conducting 10 interviews, 2 expert focus groups and a observational study. An artefact was produced during the research to emulate a potential system. During the research, the researchers identified ten heuristic usability issues within TDMS. Fur- thermore the functional and non-functional needs of Husqvarna have been identified. The artefact was created, based on existing usability guidelines, addressing the usability issues and the needs of Husqvarna. The artefact was used to answer if the applied guidelines have solved the identified usability issues. The conclusion was set, that the applied guidelines had solved the identified issues. With the research being conducted with a single case study, the result may lack generalisability. Future researchers are encouraged to conduct a multiple case study to further identify issues within the research area.

The Semantic Web, or the Web of Data, promotes common data formats for representing structured data and their links over the web. RDF is the defacto standard for semantic data where it provides a flexible semi-structured model for describing concepts and relationships. RDF datasets consist of entries (i.e, triples) that range from thousands to Billions. The astronomical growth of RDF data calls for scalable RDF management and query processing strategies. This dissertation addresses efficient query processing over web-scale RDF data. The first contribution is WORQ, an online, workload-driven, RDF query processing technique. Based on the query workload, reduced sets of intermediate results (or reductions, for short) that are common for specific join pattern(s) are computed in an online fashion. Also, we introduce an efficient solution for RDF queries with unbound properties. The second contribution is SPARTI, a scalable technique for computing the reductions offline. SPARTI utilizes a partitioning schema, termed SemVP, that enables efficient management of the reductions. SPARTI uses a budgeting mechanism with a cost model to determine the worthiness of partitioning. The third contribution is KC, an efficient RDF data management system for the cloud. KC uses generalized filtering that encompasses both exact and approximate set membership structures that are used for filtering irrelevant data. KC defines a set of common operations and introduces an efficient method for managing and constructing filters. The final contribution is semantic filtering where data can be reduced based on the spatial, temporal, or ontological aspects of a query. We present a set of encoding techniques and demonstrate how to use semantic filters to reduce irrelevant data in a distributed setting.

Made available in DSpace on 2014-06-12T15:49:23Z (GMT). No. of bitstreams: 1 license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2009 / Faculdade de Amparo à Ciência e Tecnologia do Estado de Pernambuco / Os Sistemas P2P de Gerenciamento de Dados (PDMS) são aplicações P2P avançadas que permitem aos usuários consultar, de forma transparente, várias fontes de dados distribuídas, heterogêneas e autônomas. Cada peer representa uma fonte de dados e exporta seu esquema de dados completo ou apenas uma parte dele. Tal esquema, denominado esquema exportado, representa os dados a serem compartilhados com outros peers no sistema e é comumente descrito por uma ontologia. Os dois aspectos mais estudados sobre gerenciamento de dados em PDMS estão relacionados com mapeamentos entre esquemas e processamento de consultas. Estes aspectos podem ser melhorados se os peers estiverem eficientemente dispostos na rede overlay de acordo com uma abordagem baseada em semântica. Nesse contexto, a noção de comunidade semântica de peers é bastante importante visto que permite aproximar logicamente peers com interesses comuns sobre um tópico específico. Entretanto, devido ao comportamento dinâmico dos peers, a criação e manutenção de comunidades semânticas é um aspecto desafiador no estágio atual de desenvolvimento dos PDMS. O objetivo principal desta tese é propor um processo baseado em semântica para agrupar, de modo incremental, peers semanticamente similares que compõem comunidades em um PDMS. Nesse processo, os peers são agrupados de acordo com o respectivo esquema exportado (uma ontologia) e processos de gerenciamento de ontologias (por exemplo, matching e sumarização) são utilizados para auxiliar a conexão dos peers. Uma arquitetura de PDMS é proposta para facilitar a organização semântica dos peers na rede overlay. Para obter a similaridade semântica entre duas ontologias de peers, propomos uma medida de similaridade global como saída de um processo de ontology matching. Para otimizar o matching entre ontologias, um processo automático para sumarização de ontologias também é proposto. Um simulador foi desenvolvido de acordo com a arquitetura do PDMS. Os processos de gerenciamento de ontologias propostos também foram desenvolvidos e incluídos no simulador. Experimentações de cada processo no contexto do PDMS assim como os resultados obtidos a partir dos experimentos são apresentadas

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Peer-Daten-Management-Systeme (PDMS) bestehen aus einer hochdynamischen Menge heterogener, autonomer Peers. Die Peers beantworten Anfragen einerseits gegen lokal gespeicherte Daten und reichen sie andererseits nach einer Umschreibung anhand von Schema-Mappings an benachbarte Peers weiter. Solche aufgrund fehlender zentraler Komponenten eigentlich hoch- flexiblen Systeme leiden bei zunehmender Anzahl von Peers unter erheblichen Effi- zienzproblemen. Die Gründe hierfür liegen in der massiven Redundanz der Pfade im Netzwerk der Peers und im Informationsverlust aufgrund von Projektionen entlang von Mapping-Pfaden. Anwender akzeptieren in hochskalierten Umgebungen zum Datenaustausch in vielen Anwendungsszenarien Konzessionen an die Vollständigkeit der Anfrageergebnisse. Unser Ansatz sieht in der Vollständigkeit ein Optimierungsziel und verfolgt einen Kompromiß zwischen Nutzen und Kosten der Anfragebearbeitung. Hierzu schlagen wir mehrere Strategien für Peers vor, um zu entscheiden, an welche Nachbar-Peers Anfragen weitergeleitet werden. Peers schließen dabei Mappings von der Anfragebearbeitung aus, von denen sie ein geringes Verhältnis von Ergebnisgröße zu Kosten, also geringe Effizienz erwarten. Als Basis dieser Schätzungen wenden wir selbstadaptive Histogramme über die Ergebniskardinalität an und weisen nach, daß diese in dieser hochdynamischen Umgebung ausreichende Genauigkeit aufweisen. Wir schlagen einen Kompromiß zwischen der Nutzung von Anfrageergebnissen zur Anpassung dieser Metadaten-Statistiken und der Beschneidung von Anfrageplänen vor, um den entsprechenden Zielkonflikt aufzulösen. Für eine Optimierungsstrategie, die das für die Anfragebearbeitung verwendete Zeit-Budget limitiert, untersuchen wir mehrere Varianten hinsichtlich des Effizienzsteigerungspotentials. Darüber hinaus nutzen wir mehrdimensionale Histogramme über die Überlappung zweier Datenquellen zur gezielten Verminderung der Redundanz in der Anfragebearbeitung. / Peer data management systems (PDMS) consist of a highly dynamic set of autonomous, heterogeneous peers connected with schema mappings. Queries submitted at a peer are answered with data residing at that peer and by passing the queries to neighboring peers. PDMS are the most general architecture for distributed integrated information systems. With no need for central coordination, PDMS are highly flexible. However, due to the typical massive redundancy in mapping paths, PDMS tend to be very inefficient in computing the complete query result as the number of peers increases. Additionally, information loss is cumulated along mapping paths due to selections and projections in the mappings. Users usually accept concessions on the completeness of query answers in large-scale data sharing settings. Our approach turns completeness into an optimization goal and thus trades off benefit and cost of query answering. To this end, we propose several strategies that guide peers in their decision to which neighbors rewritten queries should be sent. In effect, the peers prune mappings that are expected to contribute few data. We propose a query optimization strategy that limits resource consumption and show that it can drastically increase efficiency while still yielding satisfying completeness of the query result. To estimate the potential data contribution of mappings, we adopted self-tuning histograms for cardinality estimation. We developed techniques that ensure sufficient query feedback to adapt these statistics to massive changes in a PDMS. Additionally, histograms can serve to maintain statistics on data overlap between alternative mapping paths. Building on them, redundant query processing is reduced by avoiding overlapping areas of the multi-dimensional data space.

So far, data warehousing has often been discussed in the light of complex OLAP queries and as reporting facility for operative data. We argue that business planning as a means to generate plan data is an equally important cornerstone of a data warehouse system, and we propose it to be a first-class citizen within an OLAP engine. We introduce an abstract model describing relevant aspects of the planning process in general and the requirements it poses to a planning engine. Furthermore, we show that business planning lends itself well to parallelization and benefits from a column-store much like traditional OLAP does. We then develop a physical model specifically targeted at a highly parallel column-store, and with our implementation, we show nearly linear scaling behavior.

DBMS are a ubiquitous building block of the software stack in many complex applications. Middleware technologies, application servers and mapping approaches hide the core database technologies just like power, networking infrastructure and operating system services. Furthermore, many enterprise-critical applications demand a certain degree of quality of service (QoS) or guarantees, e.g. wrt. response time, transaction throughput, latency but also completeness or more generally quality of results. Examples of such applications are billing systems in telecommunication, where each telephone call has to be monitored and registered in a database, Ecommerce applications where orders have to be accepted even in times of heavy load and the waiting time of customers should not exceed a few seconds, ERP systems processing a large number of transactions in parallel, or systems for processing streaming or sensor data in realtime, e.g. in process automation of traffic control. As part of complex multilevel software stack, database systems have to share or contribute to these QoS requirements, which means that guarantees have to be given by the DBMS, too, and that the processing of database requests is predictable. Todays mainstream DBMS typically follow a best effort approach: requests are processed as fast as possible without any guarantees: the optimization goal of query optimizers and tuning approaches is rather to minimize resource consumption instead of just fulfilling given service level agreements. However, motivated by the situation described above there is an emerging need for database services providing guarantees or simply behave in a predictable manner and at the same time interact with other components of the software stack in order to fulfill the requirements. This is also driven by the paradigm of service-oriented architectures widely discussed in industry. Currently, this is addressed only by very specialized solutions. Nevertheless, database researchers have developed several techniques contributing to the goal of QoS-aware database systems. The purpose of the tutorial is to introduce database researchers and practitioners to the scope, the challenges and the available techniques to the problem of predictability and QoS agreements in DBMS.

Les séries temporelles deviennent omniprésentes dans la vie moderne et leur analyse de plus en plus difficile compte tenu de leur taille. L’analyse des grandes séries de données implique des tâches telles que l’appariement de modèles (motifs), la détection d’anomalies, l’identification de modèles fréquents, et la classification ou le regroupement (clustering). Ces tâches reposent sur la notion de similarité. La communauté scientifique a proposé de plusieurs techniques, y compris de nombreuses mesures de similarité pour calculer la distance entre deux séries temporelles, ainsi que des techniques et des algorithmes d’indexation correspondants, afin de relever les défis de l’évolutivité lors de la recherche de similarité.Les analystes, afin de s’acquitter efficacement de leurs tâches, ont besoin de systèmes d’analyse visuelle interactifs, extrêmement rapides, et puissants. Lors de la création de tels systèmes, nous avons identifié deux principaux défis: (1) la perception de similarité et (2) la recherche progressive de similarité. Le premier traite de la façon dont les gens perçoivent des modèles similaires et du rôle de la visualisation dans la perception de similarité. Le dernier point concerne la rapidité avec laquelle nous pouvons redonner aux utilisateurs des mises à jour des résultats progressifs, lorsque les temps de réponse du système sont longs et non interactifs. Le but de cette thèse est de répondre et de donner des solutions aux défis ci-dessus.Dans la première partie, nous avons étudié si différentes représentations visuelles (Graphiques en courbes, Graphiques d’horizon et Champs de couleur) modifiaient la perception de similarité des séries temporelles. Nous avons essayé de comprendre si les résultats de recherche automatique de similarité sont perçus de manière similaire, quelle que soit la technique de visualisation; et si ce que les gens perçoivent comme similaire avec chaque visualisation s’aligne avec différentes mesures de similarité. Nos résultats indiquent que les Graphes d’horizon s’alignent sur des mesures qui permettent des variations de décalage temporel ou d’échelle (i.e., ils promeuvent la déformation temporelle dynamique). En revanche, ils ne s’alignent pas sur des mesures autorisant des variations d’amplitude et de décalage vertical (ils ne promeuvent pas des mesures basées sur la z-normalisation). L’inverse semble être le cas pour les Graphiques en courbes et les Champs de couleur. Dans l’ensemble, nos travaux indiquent que le choix de la visualisation affecte les schémas temporels que l’homme considère comme similaires. Donc, la notion de similarité dans les séries temporelles est dépendante de la technique de visualisation.Dans la deuxième partie, nous nous sommes concentrés sur la recherche progressive de similarité dans de grandes séries de données. Nous avons étudié la rapidité avec laquelle les premières réponses approximatives et puis des mises à jour des résultats progressifs sont détectées lors de l’exécuton des requêtes progressives. Nos résultats indiquent qu’il existe un écart entre le moment où la réponse finale s’est trouvée et le moment où l’algorithme de recherche se termine, ce qui entraîne des temps d’attente gonflés sans amélioration. Des estimations probabilistes pourraient aider les utilisateurs à décider quand arrêter le processus de recherche, i.e., décider quand l’amélioration de la réponse finale est improbable. Nous avons développé et évalué expérimentalement une nouvelle méthode probabiliste qui calcule les garanties de qualité des résultats progressifs de k-plus proches voisins (k-NN). Notre approche apprend d’un ensemble de requêtes et construit des modèles de prédiction basés sur deux observations: (i) des requêtes similaires ont des réponses similaires; et (ii) des réponses progressives renvoyées par les indices de séries de données sont de bons prédicteurs de la réponse finale. Nous fournissons des estimations initiales et progressives de la réponse finale. / Time series are becoming ubiquitous in modern life, and given their sizes, their analysis is becoming increasingly challenging. Time series analysis involves tasks such as pattern matching, anomaly detection, frequent pattern identification, and time series clustering or classification. These tasks rely on the notion of time series similarity. The data-mining community has proposed several techniques, including many similarity measures (or distance measure algorithms), for calculating the distance between two time series, as well as corresponding indexing techniques and algorithms, in order to address the scalability challenges during similarity search.To effectively support their tasks, analysts need interactive visual analytics systems that combine extremely fast computation, expressive querying interfaces, and powerful visualization tools. We identified two main challenges when considering the creation of such systems: (1) similarity perception and (2) progressive similarity search. The former deals with how people perceive similar patterns and what the role of visualization is in time series similarity perception. The latter is about how fast we can give back to users updates of progressive similarity search results and how good they are, when system response times are long and do not support real-time analytics in large data series collections. The goal of this thesis, that lies at the intersection of Databases and Human-Computer Interaction, is to answer and give solutions to the above challenges.In the first part of the thesis, we studied whether different visual representations (Line Charts, Horizon Graphs, and Color Fields) alter time series similarity perception. We tried to understand if automatic similarity search results are perceived in a similar manner, irrespective of the visualization technique; and if what people perceive as similar with each visualization aligns with different automatic similarity measures and their similarity constraints. Our findings indicate that Horizon Graphs promote as invariant local variations in temporal position or speed, and as a result they align with measures that allow variations in temporal shifting or scaling (i.e., dynamic time warping). On the other hand, Horizon Graphs do not align with measures that allow amplitude and y-offset variations (i.e., measures based on z-normalization), because they exaggerate these differences, while the inverse seems to be the case for Line Charts and Color Fields. Overall, our work indicates that the choice of visualization affects what temporal patterns humans consider as similar, i.e., the notion of similarity in time series is visualization-dependent.In the second part of the thesis, we focused on progressive similarity search in large data series collections. We investigated how fast first approximate and then updates of progressive answers are detected, while we execute similarity search queries. Our findings indicate that there is a gap between the time the final answer is found and the time when the search algorithm terminates, resulting in inflated waiting times without any improvement. Computing probabilistic estimates of the final answer could help users decide when to stop the search process. We developed and experimentally evaluated using benchmarks, a new probabilistic learning-based method that computes quality guarantees (error bounds) for progressive k-Nearest Neighbour (k-NN) similarity search results. Our approach learns from a set of queries and builds prediction models based on two observations: (i) similar queries have similar answers; and (ii) progressive best-so-far (bsf) answers returned by the state-of-the-art data series indexes are good predictors of the final k-NN answer. We provide both initial and incrementally improved estimates of the final answer.

The radio astronomy and space geodesy scientific instrumentation of the Hartebeesthoek Radio Astronomy Observatory (HartRAO) in Gauteng, South Africa, generate large volumes of data. Additional large data volumes will be generated by new geodesy instruments that are currently under construction and implementation, including a lunar laser ranging (LLR) system, seismic and meteorological systems and a Very Long Baseline Interferometry (VLBI) global observing system (VGOS) radio telescope. The existing HartRAO data management and storage system is outdated, incompatible and has limited storage capacity. This necessitates the design of a new geodetic research data management system (GRDMS). The focus of this dissertation is on providing a contextual framework for the design of the new system, including criteria, characteristics, components, an infrastructure architectural model and data structuring and organisation. An exploratory research methodology and qualitative research techniques were applied. Results attained from interviews conducted and literature consulted indicates a gap in the literature regarding the design of a data management system, specifically for geodetic data generated by HartRAO instrumentation. This necessitates the development of a conceptual framework for the design of a new GRDMS. Results are in alignment with the achievement of the research questions and objectives set for this study. / Information Science / M.A. (Information Science)