8,259 research outputs found

    When Things Matter: A Data-Centric View of the Internet of Things

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    With the recent advances in radio-frequency identification (RFID), low-cost wireless sensor devices, and Web technologies, the Internet of Things (IoT) approach has gained momentum in connecting everyday objects to the Internet and facilitating machine-to-human and machine-to-machine communication with the physical world. While IoT offers the capability to connect and integrate both digital and physical entities, enabling a whole new class of applications and services, several significant challenges need to be addressed before these applications and services can be fully realized. A fundamental challenge centers around managing IoT data, typically produced in dynamic and volatile environments, which is not only extremely large in scale and volume, but also noisy, and continuous. This article surveys the main techniques and state-of-the-art research efforts in IoT from data-centric perspectives, including data stream processing, data storage models, complex event processing, and searching in IoT. Open research issues for IoT data management are also discussed

    Similarity processing in multi-observation data

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    Many real-world application domains such as sensor-monitoring systems for environmental research or medical diagnostic systems are dealing with data that is represented by multiple observations. In contrast to single-observation data, where each object is assigned to exactly one occurrence, multi-observation data is based on several occurrences that are subject to two key properties: temporal variability and uncertainty. When defining similarity between data objects, these properties play a significant role. In general, methods designed for single-observation data hardly apply for multi-observation data, as they are either not supported by the data models or do not provide sufficiently efficient or effective solutions. Prominent directions incorporating the key properties are the fields of time series, where data is created by temporally successive observations, and uncertain data, where observations are mutually exclusive. This thesis provides research contributions for similarity processing - similarity search and data mining - on time series and uncertain data. The first part of this thesis focuses on similarity processing in time series databases. A variety of similarity measures have recently been proposed that support similarity processing w.r.t. various aspects. In particular, this part deals with time series that consist of periodic occurrences of patterns. Examining an application scenario from the medical domain, a solution for activity recognition is presented. Finally, the extraction of feature vectors allows the application of spatial index structures, which support the acceleration of search and mining tasks resulting in a significant efficiency gain. As feature vectors are potentially of high dimensionality, this part introduces indexing approaches for the high-dimensional space for the full-dimensional case as well as for arbitrary subspaces. The second part of this thesis focuses on similarity processing in probabilistic databases. The presence of uncertainty is inherent in many applications dealing with data collected by sensing devices. Often, the collected information is noisy or incomplete due to measurement or transmission errors. Furthermore, data may be rendered uncertain due to privacy-preserving issues with the presence of confidential information. This creates a number of challenges in terms of effectively and efficiently querying and mining uncertain data. Existing work in this field either neglects the presence of dependencies or provides only approximate results while applying methods designed for certain data. Other approaches dealing with uncertain data are not able to provide efficient solutions. This part presents query processing approaches that outperform existing solutions of probabilistic similarity ranking. This part finally leads to the application of the introduced techniques to data mining tasks, such as the prominent problem of probabilistic frequent itemset mining.Viele Anwendungsgebiete, wie beispielsweise die Umweltforschung oder die medizinische Diagnostik, nutzen Systeme der Sensorüberwachung. Solche Systeme müssen oftmals in der Lage sein, mit Daten umzugehen, welche durch mehrere Beobachtungen repräsentiert werden. Im Gegensatz zu Daten mit nur einer Beobachtung (Single-Observation Data) basieren Daten aus mehreren Beobachtungen (Multi-Observation Data) auf einer Vielzahl von Beobachtungen, welche zwei Schlüsseleigenschaften unterliegen: Zeitliche Veränderlichkeit und Datenunsicherheit. Im Bereich der Ähnlichkeitssuche und im Data Mining spielen diese Eigenschaften eine wichtige Rolle. Gängige Lösungen in diesen Bereichen, die für Single-Observation Data entwickelt wurden, sind in der Regel für den Umgang mit mehreren Beobachtungen pro Objekt nicht anwendbar. Der Grund dafür liegt darin, dass diese Ansätze entweder nicht mit den Datenmodellen vereinbar sind oder keine Lösungen anbieten, die den aktuellen Ansprüchen an Lösungsqualität oder Effizienz genügen. Bekannte Forschungsrichtungen, die sich mit Multi-Observation Data und deren Schlüsseleigenschaften beschäftigen, sind die Analyse von Zeitreihen und die Ähnlichkeitssuche in probabilistischen Datenbanken. Während erstere Richtung eine zeitliche Ordnung der Beobachtungen eines Objekts voraussetzt, basieren unsichere Datenobjekte auf Beobachtungen, die sich gegenseitig bedingen oder ausschließen. Diese Dissertation umfasst aktuelle Forschungsbeiträge aus den beiden genannten Bereichen, wobei Methoden zur Ähnlichkeitssuche und zur Anwendung im Data Mining vorgestellt werden. Der erste Teil dieser Arbeit beschäftigt sich mit Ähnlichkeitssuche und Data Mining in Zeitreihendatenbanken. Insbesondere werden Zeitreihen betrachtet, welche aus periodisch auftretenden Mustern bestehen. Im Kontext eines medizinischen Anwendungsszenarios wird ein Ansatz zur Aktivitätserkennung vorgestellt. Dieser erlaubt mittels Merkmalsextraktion eine effiziente Speicherung und Analyse mit Hilfe von räumlichen Indexstrukturen. Für den Fall hochdimensionaler Merkmalsvektoren stellt dieser Teil zwei Indexierungsmethoden zur Beschleunigung von ähnlichkeitsanfragen vor. Die erste Methode berücksichtigt alle Attribute der Merkmalsvektoren, während die zweite Methode eine Projektion der Anfrage auf eine benutzerdefinierten Unterraum des Vektorraums erlaubt. Im zweiten Teil dieser Arbeit wird die Ähnlichkeitssuche im Kontext probabilistischer Datenbanken behandelt. Daten aus Sensormessungen besitzen häufig Eigenschaften, die einer gewissen Unsicherheit unterliegen. Aufgrund von Mess- oder übertragungsfehlern sind gemessene Werte oftmals unvollständig oder mit Rauschen behaftet. In diversen Szenarien, wie beispielsweise mit persönlichen oder medizinisch vertraulichen Daten, können Daten auch nachträglich von Hand verrauscht werden, so dass eine genaue Rekonstruktion der ursprünglichen Informationen nicht möglich ist. Diese Gegebenheiten stellen Anfragetechniken und Methoden des Data Mining vor einige Herausforderungen. In bestehenden Forschungsarbeiten aus dem Bereich der unsicheren Datenbanken werden diverse Probleme oftmals nicht beachtet. Entweder wird die Präsenz von Abhängigkeiten ignoriert, oder es werden lediglich approximative Lösungen angeboten, welche die Anwendung von Methoden für sichere Daten erlaubt. Andere Ansätze berechnen genaue Lösungen, liefern die Antworten aber nicht in annehmbarer Laufzeit zurück. Dieser Teil der Arbeit präsentiert effiziente Methoden zur Beantwortung von Ähnlichkeitsanfragen, welche die Ergebnisse absteigend nach ihrer Relevanz, also eine Rangliste der Ergebnisse, zurückliefern. Die angewandten Techniken werden schließlich auf Problemstellungen im probabilistischen Data Mining übertragen, um beispielsweise das Problem des Frequent Itemset Mining unter Berücksichtigung des vollen Gehalts an Unsicherheitsinformation zu lösen

    Monitoring data streams

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    Stream monitoring is concerned with analyzing data that is represented in the form of infinite streams. This field has gained prominence in recent years, as streaming data is generated in increasing volume and dimension in a variety of areas. It finds application in connection with monitoring industrial sensors, "smart" technology like smart houses and smart cars, wearable devices used for medical and physiological monitoring, but also in environmental surveillance or finance. However, stream monitoring is a challenging task due to the diverse and changing nature of the streaming data, its high volume and high dimensionality with thousands of sensors producing streams with millions of measurements over short time spans. Automated, scalable and efficient analysis of these streams can help to keep track of important events, highlight relevant aspects and provide better insights into the monitored system. In this thesis, we propose techniques adapted to these tasks in supervised and unsupervised settings, in particular Stream Classification and Stream Dependency Monitoring. After a motivating introduction, we introduce concepts related to streaming data and discuss technological frameworks that have emerged to deal with streaming data in the second chapter of this thesis. We introduce the notion of information theoretical entropy as a useful basis for data monitoring in the third chapter. In the second part of the thesis, we present Probabilistic Hoeffding Trees, a novel approach towards stream classification. We will show how probabilistic learning greatly improves the flexibility of decision trees and their ability to adapt to changes in data streams. The general technique is applicable to a variety of classification models and fast to compute without significantly greater memory cost compared to regular Hoeffding Trees. We show that our technique achieves better or on-par results to current state-of-the-art tree classification models on a variety of large, synthetic and real life data sets. In the third part of the thesis, we concentrate on unsupervised monitoring of data streams. We will use mutual information as entropic measure to identify the most important relationships in a monitored system. By using the powerful concept of mutual information we can, first, capture relevant aspects in a great variety of data sources with different underlying concepts and possible relationships and, second, analyze theoretical and computational complexity. We present the MID and DIMID algorithms. They perform extremely efficient on high dimensional data streams and provide accurate results, outperforming state-of-the-art algorithms for dependency monitoring. In the fourth part of this thesis, we introduce delayed relationships as a further feature in the dependency analysis. In reality, the phenomena monitored by e.g. some type of sensor might depend on another, but measurable effects can be delayed. This delay might be due to technical reasons, i.e. different stream processing speeds, or because the effects actually appear delayed over time. We present Loglag, the first algorithm that monitors dependency with respect to an optimal delay. It utilizes several approximation techniques to achieve competitive resource requirements. We demonstrate its scalability and accuracy on real world data, and also give theoretical guarantees to its accuracy

    Fast Online Similarity Search for Uncertain Time Series

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    To achieve fast retrieval of online data, it is needed for the retrieval algorithm to increase throughput while reducing latency. Based on the traditional online processing algorithm for time series data, we propose a spatial index structure that can be updated and searched quickly in a real-time environment. At the same time, we introduce an adaptive segmentation method to divide the space corresponding to nodes. Unlike traditional retrieval algorithms, for uncertain time series, the distance threshold used for screening will dynamically change due to noise during the search process. Extensive experiments are conducted to compare the accuracy of the query results and the timeliness of the algorithm. The results show that the index structure proposed in this paper has better efficiency while maintaining a similar true positive ratio
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