Accurate Data Approximation in Constrained Environments

Several data reduction techniques have been proposed recently as methods for providing fast and fairly accurate answers to complex queries over large quantities of data. Their use has been widespread, due to the multiple benefits that they may offer in several constrained environments and applications. Compressed data representations require less space to store, less bandwidth to communicate and can provide, due to their size, very fast response times to queries. Sensor networks represent a typical constrained environment, due to the limited processing, storage and battery capabilities of the sensor nodes. Large-scale sensor networks require tight data handling and data dissemination techniques. Transmitting a full-resolution data feed from each sensor back to the base-station is often prohibitive due to (i) limited bandwidth that may not be sufficient to sustain a continuous feed from all sensors and (ii) increased power consumption due to the wireless multi-hop communication. In order to minimize the volume of the transmitted data, we can apply two well data reduction techniques: aggregation and approximation. In this dissertation we propose novel data reduction techniques for the transmission of measurements collected in sensor network environments. We first study the problem of summarizing multi-valued data feeds generated at a single sensor node, a step necessary for the transmission of large amounts of historical information collected at the node. The transmission of these measurements may either be periodic (i.e., when a certain amount of measurements has been collected), or in response to a query from the base station. We then also consider the approximate evaluation of aggregate continuous queries. A continuous query is a query that runs continuously until explicitly terminated by the user. These queries can be used to obtain a live-estimate of some (aggregated) quantity, such as the total number of moving objects detected by the sensors

Mining complex data in highly streaming environments

Data is growing at a rapid rate because of advanced hardware and software technologies and platforms such as e-health systems, sensor networks, and social media. One of the challenging problems is storing, processing and transferring this big data in an efficient and effective way. One solution to tackle these challenges is to construct synopsis by means of data summarization techniques. Motivated by the fact that without summarization, processing, analyzing and communicating this vast amount of data is inefficient, this thesis introduces new summarization frameworks with the main goals of reducing communication costs and accelerating data mining processes in different application scenarios. Specifically, we study the following big data summarizaion techniques:(i) dimensionality reduction;(ii)clustering,and(iii)histogram, considering their importance and wide use in various areas and domains. In our work, we propose three different frameworks using these summarization techniques to cover three different aspects of big data:"Volume","Velocity"and"Variety" in centralized and decentralized platforms. We use dimensionality reduction techniques for summarizing large 2D-arrays, clustering and histograms for processing multiple data streams. With respect to the importance and rapid growth of emerging e-health applications such as tele-radiology and tele-medicine that require fast, low cost, and often lossless access to massive amounts of medical images and data over band limited channels,our first framework attempts to summarize streams of large volume medical images (e.g. X-rays) for the purpose of compression. Significant amounts of correlation and redundancy exist across different medical images. These can be extracted and used as a data summary to achieve better compression, and consequently less storage and less communication overheads on the network. We propose a novel memory-assisted compression framework as a learning-based universal coding, which can be used to complement any existing algorithm to further eliminate redundancies/similarities across images. This approach is motivated by the fact that, often in medical applications, massive amounts of correlated images from the same family are available as training data for learning the dependencies and deriving appropriate reference or synopses models. The models can then be used for compression of any new image from the same family. In particular, dimensionality reduction techniques such as Principal Component Analysis (PCA) and Non-negative Matrix Factorization (NMF) are applied on a set of images from training data to form the required reference models. The proposed memory-assisted compression allows each image to be processed independently of other images, and hence allows individual image access and transmission. In the second part of our work,we investigate the problem of summarizing distributed multidimensional data streams using clustering. We devise a distributed clustering framework, DistClusTree, that extends the centralized ClusTree approach. The main difficulty in distributed clustering is balancing communication costs and clustering quality. We tackle this in DistClusTree through combining spatial index summaries and online tracking for efficient local and global incremental clustering. We demonstrate through extensive experiments the efficacy of the framework in terms of communication costs and approximate clustering quality. In the last part, we use a multidimensional index structure to merge distributed summaries in the form of a centralized histogram as another widely used summarization technique with the application in approximate range query answering. In this thesis, we propose the index-based Distributed Mergeable Summaries (iDMS) framework based on kd-trees that addresses these challenges with data generative models of Gaussian mixture models (GMMs) and a Generative Adversarial Network (GAN). iDMS maintains a global approximate kd-tree at a central site via GMMs or GANs upon new arrivals of streaming data at local sites. Experimental results validate the effectiveness and efficiency of iDMS against baseline distributed settings in terms of approximation error and communication costs

Query estimation techniques in database systems

The effctiveness of query optimization in database systems critically depends on the system';s ability to assess the execution costs of different query execution plans. For this purpose, the sizes and data distributions of the intermediate results generated during plan execution need to be estimated as accurately as possible. This estimation requires the maintenance of statistics on the data stored in the database, which are referred to as data synopses. While the problem of query cost estimation has received significant attention for over a decade, it has remained an open issue in practice, because most previous techniques have focused on singular aspects of the problem such as minimizing the estimation error of a single type of query and a single data distribution, whereas database management systems generally need to support a wide range of queries over a number of datasets. In this thesis I introduce a new technique for query result estimation, which extends existing techniques in that it offers estimation for all combinations of the three major database operators selection, projection, and join. The approach is based on separate and independent approximations of the attribute values contained in a dataset and their frequencies. Through the use of space-filling curves, the approach extends to multi-dimensional data, while maintaining its accuracy and computational properties. The resulting estimation accuracy is competitive with specialized techniques and superior to the histogram techniques currently implemented in commercial database management systems. Because data synopses reside in main memory, they compete for available space with the database cache and query execution buffers. Consequently, the memory available to data synopses needs to be used efficiently. This results in a physical design problem for data synopses, which is to determine the best set of synopses for a given combination of datasets, queries, and available memory. This thesis introduces a formalization of the problem, and efficient algorithmic solutions. All discussed techniques are evaluated with regard to their overhead and resulting estimation accuracy on a variety of synthetic and real-life datasets.Die Effektivität der Anfrage-Optimierung in Datenbanksystemen hängt entscheidend von der Fähigkeit des Systems ab, die Kosten der verschiedenen Möglichkeiten, eine Anfrage auszuführen, abzuschätzen. Zu diesem Zweck ist es nötig, die Größen und Datenverteilungen der Zwischenresultate, die während der Ausführung einer Anfrage generiert werden, so genau wie möglich zu schätzen. Zur Lösung dieses Schätzproblems benötigt man Statistiken über die Daten, welche in dem Datenbanksystem gespeichert werden; diese Statistiken werden auch als Daten Synopsen bezeichnet. Obwohl das Problem der Schätzung von Anfragekosten innerhalb der letzten 10 Jahre intensiv untersucht wurde, gilt es weiterhin als offen, da viele der vorgeschlagenen Ansätze nur einen Teilaspekt des Problems betrachten. In den meisten Fällen wurden Techniken für das Abschätzen eines einzelnen Operators auf einer einzelnen Datenverteilung untersucht, wohingegen Datenbanksysteme in der Praxis eine Vielfalt von Anfragen über diverse Datensätze unterstützen müssen. Aus diesem Grund stellt diese Arbeit einen neuen Ansatz zur Resultatsabschätzung vor, welcher insofern über bestehende Ansätze hinausgeht, als dass er akkurate Abschätzung beliebiger Kombinationen der drei wichtigsten Datenbank-Operatoren erlaubt: Selektion, Projektion und Join. Meine Technik basiert auf separaten und unabhängigen Approximationen der Verteilung der Attributwerte eines Datensatzes und der Verteilung der Häufigkeiten dieser Attributwerte. Durch den Einsatz raumfüllender Kurven können diese Approximationstechniken zudem auf mehrdimensionale Datenverteilungen angewandt werden, ohne ihre Genauigkeit und geringen Berechnungskosten einzubüßen. Die resultierende Schätzgenauigkeit ist vergleichbar mit der von auf einen einzigen Operator spezialisierten Techniken, und deutlich höher als die der auf Histogrammen basierenden Ansätze, welche momentan in kommerziellen Datenbanksystemen eingesetzt werden. Da Daten Synopsen im Arbeitsspeicher residieren, reduzieren sie den Speicher, der für den Seitencache oder Ausführungspuffer zur Verfügung steht. Somit sollte der für Synopsen reservierte Speicher effizient genutzt werden, bzw. möglichst klein sein. Dies führt zu dem Problem, die optimale Kombination von Synopsen für eine gegebene Kombination an Daten, Anfragen und verfügbarem Speicher zu bestimmen. Diese Arbeit stellt eine formale Beschreibung des Problems, sowie effiziente Algorithmen zu dessen Lösung vor. Alle beschriebenen Techniken werden in Hinsicht auf ihren Aufwand und die resultierende Schätzgenauigkeit mittels Experimenten über eine Vielzahl von Datenverteilungen evaluiert

Query estimation techniques in database systems

The effctiveness of query optimization in database systems critically depends on the system\u27;s ability to assess the execution costs of different query execution plans. For this purpose, the sizes and data distributions of the intermediate results generated during plan execution need to be estimated as accurately as possible. This estimation requires the maintenance of statistics on the data stored in the database, which are referred to as data synopses. While the problem of query cost estimation has received significant attention for over a decade, it has remained an open issue in practice, because most previous techniques have focused on singular aspects of the problem such as minimizing the estimation error of a single type of query and a single data distribution, whereas database management systems generally need to support a wide range of queries over a number of datasets. In this thesis I introduce a new technique for query result estimation, which extends existing techniques in that it offers estimation for all combinations of the three major database operators selection, projection, and join. The approach is based on separate and independent approximations of the attribute values contained in a dataset and their frequencies. Through the use of space-filling curves, the approach extends to multi-dimensional data, while maintaining its accuracy and computational properties. The resulting estimation accuracy is competitive with specialized techniques and superior to the histogram techniques currently implemented in commercial database management systems. Because data synopses reside in main memory, they compete for available space with the database cache and query execution buffers. Consequently, the memory available to data synopses needs to be used efficiently. This results in a physical design problem for data synopses, which is to determine the best set of synopses for a given combination of datasets, queries, and available memory. This thesis introduces a formalization of the problem, and efficient algorithmic solutions. All discussed techniques are evaluated with regard to their overhead and resulting estimation accuracy on a variety of synthetic and real-life datasets.Die Effektivität der Anfrage-Optimierung in Datenbanksystemen hängt entscheidend von der Fähigkeit des Systems ab, die Kosten der verschiedenen Möglichkeiten, eine Anfrage auszuführen, abzuschätzen. Zu diesem Zweck ist es nötig, die Größen und Datenverteilungen der Zwischenresultate, die während der Ausführung einer Anfrage generiert werden, so genau wie möglich zu schätzen. Zur Lösung dieses Schätzproblems benötigt man Statistiken über die Daten, welche in dem Datenbanksystem gespeichert werden; diese Statistiken werden auch als Daten Synopsen bezeichnet. Obwohl das Problem der Schätzung von Anfragekosten innerhalb der letzten 10 Jahre intensiv untersucht wurde, gilt es weiterhin als offen, da viele der vorgeschlagenen Ansätze nur einen Teilaspekt des Problems betrachten. In den meisten Fällen wurden Techniken für das Abschätzen eines einzelnen Operators auf einer einzelnen Datenverteilung untersucht, wohingegen Datenbanksysteme in der Praxis eine Vielfalt von Anfragen über diverse Datensätze unterstützen müssen. Aus diesem Grund stellt diese Arbeit einen neuen Ansatz zur Resultatsabschätzung vor, welcher insofern über bestehende Ansätze hinausgeht, als dass er akkurate Abschätzung beliebiger Kombinationen der drei wichtigsten Datenbank-Operatoren erlaubt: Selektion, Projektion und Join. Meine Technik basiert auf separaten und unabhängigen Approximationen der Verteilung der Attributwerte eines Datensatzes und der Verteilung der Häufigkeiten dieser Attributwerte. Durch den Einsatz raumfüllender Kurven können diese Approximationstechniken zudem auf mehrdimensionale Datenverteilungen angewandt werden, ohne ihre Genauigkeit und geringen Berechnungskosten einzubüßen. Die resultierende Schätzgenauigkeit ist vergleichbar mit der von auf einen einzigen Operator spezialisierten Techniken, und deutlich höher als die der auf Histogrammen basierenden Ansätze, welche momentan in kommerziellen Datenbanksystemen eingesetzt werden. Da Daten Synopsen im Arbeitsspeicher residieren, reduzieren sie den Speicher, der für den Seitencache oder Ausführungspuffer zur Verfügung steht. Somit sollte der für Synopsen reservierte Speicher effizient genutzt werden, bzw. möglichst klein sein. Dies führt zu dem Problem, die optimale Kombination von Synopsen für eine gegebene Kombination an Daten, Anfragen und verfügbarem Speicher zu bestimmen. Diese Arbeit stellt eine formale Beschreibung des Problems, sowie effiziente Algorithmen zu dessen Lösung vor. Alle beschriebenen Techniken werden in Hinsicht auf ihren Aufwand und die resultierende Schätzgenauigkeit mittels Experimenten über eine Vielzahl von Datenverteilungen evaluiert

Low-latency, query-driven analytics over voluminous multidimensional, spatiotemporal datasets

2017 Summer.Includes bibliographical references.Ubiquitous data collection from sources such as remote sensing equipment, networked observational devices, location-based services, and sales tracking has led to the accumulation of voluminous datasets; IDC projects that by 2020 we will generate 40 zettabytes of data per year, while Gartner and ABI estimate 20-35 billion new devices will be connected to the Internet in the same time frame. The storage and processing requirements of these datasets far exceed the capabilities of modern computing hardware, which has led to the development of distributed storage frameworks that can scale out by assimilating more computing resources as necessary. While challenging in its own right, storing and managing voluminous datasets is only the precursor to a broader field of study: extracting knowledge, insights, and relationships from the underlying datasets. The basic building block of this knowledge discovery process is analytic queries, encompassing both query instrumentation and evaluation. This dissertation is centered around query-driven exploratory and predictive analytics over voluminous, multidimensional datasets. Both of these types of analysis represent a higher-level abstraction over classical query models; rather than indexing every discrete value for subsequent retrieval, our framework autonomously learns the relationships and interactions between dimensions in the dataset (including time series and geospatial aspects), and makes the information readily available to users. This functionality includes statistical synopses, correlation analysis, hypothesis testing, probabilistic structures, and predictive models that not only enable the discovery of nuanced relationships between dimensions, but also allow future events and trends to be predicted. This requires specialized data structures and partitioning algorithms, along with adaptive reductions in the search space and management of the inherent trade-off between timeliness and accuracy. The algorithms presented in this dissertation were evaluated empirically on real-world geospatial time-series datasets in a production environment, and are broadly applicable across other storage frameworks

Monitoring Network Data Streams

Ph.DDOCTOR OF PHILOSOPH

Topics in Massive Data Summarization.

We consider three problems in this thesis. First, we want to construct a nearly workload-optimal histogram. Given B, we want to find the near optimal B bucket histogram under associated workload w within 1 + epsilon error tolerance. In the cash register model where data is streamed as a series of updates, we can build a histogram using polylogarithmic space, polylogarithmic time to process each item, and polylogarithmic post-processing time to build the histogram. All these results need the workload to be explicitly stored since we show that if the workload is summarized in small space lossily, algorithmic results such as above do not exist. Then, we consider the problem of private computation of approximate Heavy Hitters. Alice and Bob each hold a vector and, in the vector sum, they want to find the B largest values along with their indices. We show how to solve the problem privately with polylogarithmic communication, polynomial work and constantly many rounds in the sense that nothing is learned by Alice and Bob beyond what is implied by their input, the ideal top-B output, and goodness of approximation (equivalently,the Euclidean norm of the vector sum). We give lower bounds showing that the Euclidean norm must leak by any efficient algorithm. In the third problem, we want to build a near optimal histogram on probabilistic data streams. Given B, we want to find the near optimal B bucket histogram on probabilistic data streams under both L1 measurement and L2 measurement. We give deterministic algorithms without sampling. We can build histograms using poly-logarithmic space, polylogarithmic time to process each item, and polylogarithmic post-processing time to build the histogram. The result we give under L2 measurement is within 1 + epsilon error tolerance, and the result under L1 measurement is heuristic. We also give a direction to give guarantees to the heuristic.Ph.D.Computer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/60841/1/xuanzh_1.pd

Behaviour Profiling using Wearable Sensors for Pervasive Healthcare

In recent years, sensor technology has advanced in terms of hardware sophistication and miniaturisation. This has led to the incorporation of unobtrusive, low-power sensors into networks centred on human participants, called Body Sensor Networks. Amongst the most important applications of these networks is their use in healthcare and healthy living. The technology has the possibility of decreasing burden on the healthcare systems by providing care at home, enabling early detection of symptoms, monitoring recovery remotely, and avoiding serious chronic illnesses by promoting healthy living through objective feedback. In this thesis, machine learning and data mining techniques are developed to estimate medically relevant parameters from a participant‘s activity and behaviour parameters, derived from simple, body-worn sensors. The first abstraction from raw sensor data is the recognition and analysis of activity. Machine learning analysis is applied to a study of activity profiling to detect impaired limb and torso mobility. One of the advances in this thesis to activity recognition research is in the application of machine learning to the analysis of 'transitional activities': transient activity that occurs as people change their activity. A framework is proposed for the detection and analysis of transitional activities. To demonstrate the utility of transition analysis, we apply the algorithms to a study of participants undergoing and recovering from surgery. We demonstrate that it is possible to see meaningful changes in the transitional activity as the participants recover. Assuming long-term monitoring, we expect a large historical database of activity to quickly accumulate. We develop algorithms to mine temporal associations to activity patterns. This gives an outline of the user‘s routine. Methods for visual and quantitative analysis of routine using this summary data structure are proposed and validated. The activity and routine mining methodologies developed for specialised sensors are adapted to a smartphone application, enabling large-scale use. Validation of the algorithms is performed using datasets collected in laboratory settings, and free living scenarios. Finally, future research directions and potential improvements to the techniques developed in this thesis are outlined

Efficient Indexing for Structured and Unstructured Data

The collection of digital data is growing at an exponential rate. Data originates from wide range of data sources such as text feeds, biological sequencers, internet traffic over routers, through sensors and many other sources. To mine intelligent information from these sources, users have to query the data. Indexing techniques aim to reduce the query time by preprocessing the data. Diversity of data sources in real world makes it imperative to develop application specific indexing solutions based on the data to be queried. Data can be structured i.e., relational tables or unstructured i.e., free text. Moreover, increasingly many applications need to seamlessly analyze both kinds of data making data integration a central issue. Integrating text with structured data needs to account for missing values, errors in the data etc. Probabilistic models have been proposed recently for this purpose. These models are also useful for applications where uncertainty is inherent in data e.g. sensor networks. This dissertation aims to propose efficient indexing solutions for several problems that lie at the intersection of database and information retrieval such as joining ranked inputs, full-text documents searching etc. Other well-known problems of ranked retrieval and pattern matching are also studied under probabilistic settings. For each problem, the worst-case theoretical bounds of the proposed solutions are established and/or their practicality is demonstrated by thorough experimentation