Quality of Service Aware Data Stream Processing for Highly Dynamic and Scalable Applications

Huge amounts of georeferenced data streams are arriving daily to data stream management systems that are deployed for serving highly scalable and dynamic applications. There are innumerable ways at which those loads can be exploited to gain deep insights in various domains. Decision makers require an interactive visualization of such data in the form of maps and dashboards for decision making and strategic planning. Data streams normally exhibit fluctuation and oscillation in arrival rates and skewness. Those are the two predominant factors that greatly impact the overall quality of service. This requires data stream management systems to be attuned to those factors in addition to the spatial shape of the data that may exaggerate the negative impact of those factors. Current systems do not natively support services with quality guarantees for dynamic scenarios, leaving the handling of those logistics to the user which is challenging and cumbersome. Three workloads are predominant for any data stream, batch processing, scalable storage and stream processing. In this thesis, we have designed a quality of service aware system, SpatialDSMS, that constitutes several subsystems that are covering those loads and any mixed load that results from intermixing them. Most importantly, we natively have incorporated quality of service optimizations for processing avalanches of geo-referenced data streams in highly dynamic application scenarios. This has been achieved transparently on top of the codebases of emerging de facto standard best-in-class representatives, thus relieving the overburdened shoulders of the users in the presentation layer from having to reason about those services. Instead, users express their queries with quality goals and our system optimizers compiles that down into query plans with an embedded quality guarantee and leaves logistic handling to the underlying layers. We have developed standard compliant prototypes for all the subsystems that constitutes SpatialDSMS

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

Scalable Model-Based Management of Correlated Dimensional Time Series in ModelarDB+

To monitor critical infrastructure, high quality sensors sampled at a high frequency are increasingly used. However, as they produce huge amounts of data, only simple aggregates are stored. This removes outliers and fluctuations that could indicate problems. As a remedy, we present a model-based approach for managing time series with dimensions that exploits correlation in and among time series. Specifically, we propose compressing groups of correlated time series using an extensible set of model types within a user-defined error bound (possibly zero). We name this new category of model-based compression methods for time series Multi-Model Group Compression (MMGC). We present the first MMGC method GOLEMM and extend model types to compress time series groups. We propose primitives for users to effectively define groups for differently sized data sets, and based on these, an automated grouping method using only the time series dimensions. We propose algorithms for executing simple and multi-dimensional aggregate queries on models. Last, we implement our methods in the Time Series Management System (TSMS) ModelarDB (ModelarDB+). Our evaluation shows that compared to widely used formats, ModelarDB+ provides up to 13.7 times faster ingestion due to high compression, 113 times better compression due to the adaptivity of GOLEMM, 630 times faster aggregates by using models, and close to linear scalability. It is also extensible and supports online query processing.Comment: 12 Pages, 28 Figures, and 1 Tabl