On the analysis of big data indexing execution strategies

Efficient response to search queries is very crucial for data analysts to obtain timely results from big data spanned over heterogeneous machines. Currently, a number of big-data processing frameworks are available in which search operations are performed in distributed and parallel manner. However, implementation of indexing mechanism results in noticeable reduction of overall query processing time. There is an urge to assess the feasibility and impact of indexing towards query execution performance. This paper investigates the performance of state-of-the-art clustered indexing approaches over Hadoop framework which is de facto standard for big data processing. Moreover, this study leverages a comparative analysis of non-clustered indexing overhead in terms of time and space taken by indexing process for varying volume data sets with increasing Index Hit Ratio. Furthermore, the experiments evaluate performance of search operations in terms of data access and retrieval time for queries that use indexes. We then validated the obtained results using Petri net mathematical modeling. We used multiple data sets in our experiments to manifest the impact of growing volume of data on indexing and data search and retrieval performance. The results and highlighted challenges favorably lead researchers towards improved implication of indexing mechanism in perspective of data retrieval from big data. Additionally, this study advocates selection of a non-clustered indexing solution so that optimized search performance over big data is obtained

SmallClient for big data: an indexing framework towards fast data retrieval

Numerous applications are continuously generating massive amount of data and it has become critical to extract useful information while maintaining acceptable computing performance. The objective of this work is to design an indexing framework which minimizes indexing overhead and improves query execution and data search performance with optimum aggregation of computing performance. We propose Small-Client, an indexing framework to speed up query execution. SmallClient has three modules: block creation, index creation and query execution. Block creation module supports improving data retrieval performance with minimum data uploading overhead. Index creation module allows maximum indexes on a dataset to increase index hit ratio with minimized indexing overhead. Finally, query execution module offers incoming queries to utilize these indexes. The evaluation shows that Small-Client outperforms Hadoop full scan with more than 90% search performance. Meanwhile, indexing overhead of SmallClient is reduced to approximately 50% and 80% for index size and indexing time respectively

Analysis of outsourcing data to the cloud using autonomous key generation

Cloud computing, a technology that enables users to store and manage their data at a low cost and high availability, has been emerging for the past few decades because of the many services it provides. One of the many services cloud computing provides to its users is data storage. The majority of the users of this service are still concerned to outsource their data due to the integrity and confidentiality issues, as well as performance and cost issues, that come along with it. These issues make it necessary to encrypt data prior to outsourcing it to the cloud. However, encrypting data prior to outsourcing makes searching the data obsolete, lowering the functionality of the cloud. Most existing cloud storage schemes often prioritize security over performance and functionality, or vice versa. In this thesis, the cloud storage service is explored, and the aspects of security, performance, and functionality are analyzed in order to investigate the trade-offs of the service. DSB-SEIS, a scheme with encryption intensity selection, an autonomous key generation algorithm that allows users to control the encryption intensity of their files, as well as other features is developed in order to find a balance between performance, security, and functionality. The features that DSB-SEIS contains are deduplication, assured deletion, and searchable encryption. The effect of encryption intensity selection on encryption, decryption, and key generation is explored, and the performance and security of DSB-SEIS are evaluated. The MapReduce framework is also used to investigate the DSB-SEIS algorithm performance with big data. Analysis demonstrates that the encryption intensity selection algorithm generates a manageable number of encryption keys based on the confidentiality of data while not adding significant overhead on encryption or decryption --Abstract, page iii

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

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

Density-Aware Linear Algebra in a Column-Oriented In-Memory Database System

Linear algebra operations appear in nearly every application in advanced analytics, machine learning, and of various science domains. Until today, many data analysts and scientists tend to use statistics software packages or hand-crafted solutions for their analysis. In the era of data deluge, however, the external statistics packages and custom analysis programs that often run on single-workstations are incapable to keep up with the vast increase in data volume and size. In particular, there is an increasing demand of scientists for large scale data manipulation, orchestration, and advanced data management capabilities. These are among the key features of a mature relational database management system (DBMS). With the rise of main memory database systems, it now has become feasible to also consider applications that built up on linear algebra. This thesis presents a deep integration of linear algebra functionality into an in-memory column-oriented database system. In particular, this work shows that it has become feasible to execute linear algebra queries on large data sets directly in a DBMS-integrated engine (LAPEG), without the need of transferring data and being restricted by hard disc latencies. From various application examples that are cited in this work, we deduce a number of requirements that are relevant for a database system that includes linear algebra functionality. Beside the deep integration of matrices and numerical algorithms, these include optimization of expressions, transparent matrix handling, scalability and data-parallelism, and data manipulation capabilities. These requirements are addressed by our linear algebra engine. In particular, the core contributions of this thesis are: firstly, we show that the columnar storage layer of an in-memory DBMS yields an easy adoption of efficient sparse matrix data types and algorithms. Furthermore, we show that the execution of linear algebra expressions significantly benefits from different techniques that are inspired from database technology. In a novel way, we implemented several of these optimization strategies in LAPEG’s optimizer (SpMachO), which uses an advanced density estimation method (SpProdest) to predict the matrix density of intermediate results. Moreover, we present an adaptive matrix data type AT Matrix to obviate the need of scientists for selecting appropriate matrix representations. The tiled substructure of AT Matrix is exploited by our matrix multiplication to saturate the different sockets of a multicore main-memory platform, reaching up to a speed-up of 6x compared to alternative approaches. Finally, a major part of this thesis is devoted to the topic of data manipulation; where we propose a matrix manipulation API and present different mutable matrix types to enable fast insertions and deletes. We finally conclude that our linear algebra engine is well-suited to process dynamic, large matrix workloads in an optimized way. In particular, the DBMS-integrated LAPEG is filling the linear algebra gap, and makes columnar in-memory DBMS attractive as efficient, scalable ad-hoc analysis platform for scientists

OctopusDB : flexible and scalable storage management for arbitrary database engines

We live in a dynamic age with the economy, the technology, and the people around us changing faster than ever before. Consequently, the data management needs in our modern world are much different than those envisioned by the early database inventors in the 70s. Today, enterprises face the challenge of managing ever-growing dataset sizes with dynamically changing query workloads. As a result, modern data managing systems, including relational as well as big data management systems, can no longer afford to be carved-in-stone solutions. Instead, data managing systems must inherently provide flexible data management techniques in order to cope with the constantly changing business needs. The current practice to deal with changing query workloads is to have a different specialized product for each workload type, e.g. row stores for OLTP workload, column stores for OLAP workload, streaming systems for streaming workload, and scan-oriented systems for shared query processing. However, this means that the enterprises have to now glue different data managing products together and copy data from one product to another, in order to support several query workloads. This has the additional penalty of managing a zoo of data managing systems in the first place, which is tedious, expensive, as well as counter-productive for modern enterprises. This thesis presents an alternative approach to supporting several query workloads in a data managing system. We observe that each specialized database product has a different data store, indicating that different query workloads work well with different data layouts. Therefore, a key requirement for supporting several query workloads is to support several data layouts. Therefore, in this thesis, we study ways to inject different data layouts into existing (and familiar) data managing systems. The goal is to develop a flexible storage layer which can support several query workloads in a single data managing system. We present a set of non-invasive techniques, coined Trojan Techniques, to inject different data layouts into a data managing system. The core idea of Trojan Techniques is to drop the assumption of having one fixed data store per data managing system. Trojan Techniques are non-invasive in the sense that they do not make heavy untenable changes to the system. Rather, they affect the data managing system from inside, almost at the core. As a result, Trojan Techniques bring significant improvements in query performance. It is interesting to note that in our approach we follow a design pattern that has been used in other non-invasive research works as well, such as PAX, fractal prefetching B+-trees, and RowCol. We propose four Trojan Techniques. First, Trojan Indexes add an additional index access path in Hadoop MapReduce. Second, Trojan Joins allow for co-partitioned joins in Hadoop MapReduce. Third, Trojan Layouts allow for row, column, or column-grouped layouts in Hadoop MapReduce. Together, these three techniques provide a highly flexible data storage layer for Hadoop MapReduce. Our final proposal, Trojan Columns, introduces columnar functionality in row-oriented relational databases, including closed source commercial databases, thus bridging the gap between row and column oriented databases. Our experimental results show that Trojan Techniques can improve the performance of Hadoop MapReduce by a factor of up to 18, and that of a top-notch commercial database product by a factor of up to 17.Wir leben in einer dynamischen Zeit, in der sich Wirtschaft, Technologie und Gesellschaft schneller verändern als jemals zuvor. Folglich unterscheiden sich die Anforderungen an Datenverarbeitung heute sehr von dem, was sich die Pioniere dieses Forschungsgebiets in den 70er Jahren ursprünglich ausgemalt hatten. Heutzutage sehen sich Firmen mit der Herausforderung konfrontiert, stark fluktuierende Anfragelasten über einer stetig wachsender Datenmengen zu bewältigen. Daher können es sich moderne Datenbanksysteme, sowohl relationale als auch Big Data Systeme, nicht mehr leisten, wie starre, in Stein gemeißelte Lösungen zu funktionieren. Stattdessen sollten moderne Datenbanksysteme von Grunde auf für flexible Datenverwaltung konzipiert werden, um mit sich ständig ändernden Anforderungen Schritt halten zu können. Die gegenwärtige Praxis im Umgang mit häufig wechselnden Anfragemustern besteht allerdings noch darin, jeweils unterschiedliche, spezialisierte Lösungen für die verschiedenen Anfragetypen zu nutzen - zum Beispiel zeilenorientierte Systeme für OLTP Anfragen, spaltenorientierte Systeme für OLAP Anfragen, Data Stream Management Systeme für kontinuierliche Datenströme und Scan-basierte Systeme für die Bearbeitung von vielen gleichzeitigen Anfragen. Leider setzt dieses Vorgehen aber voraus, dass die Unternehmen es schaffen die verschiedensten Systeme irgendwie miteinander zu verknüpfen und einen Datenaustausch zwischen ihnen zu gewährleisten. Ein zusätzlicher Nachteil ist, dass hierbei oft ein ganzes Sortiment von Datenbankprodukten eingerichtet und gepflegt werden muss, was sowohl zeit- als auch kostenintensiv und damit letztlich aufwendig ist. Diese Dissertation präsentiert eine alternative Lösung, um wechselnde Anfragemuster effizient mit einem einzigen Datenverwaltungssystem zu unterstützen. Aus der Beobachtung, dass jedes spezielle Datenbankprodukt unterschiedliche Ansätze zur Datenspeicherung nutzt, folgern wir, dass verschiedene Anfragen jeweils auf bestimmten Datenlayouts effizienter beantwortet werden können als auf anderen. Deshalb ist eine zentrale Anforderung zur effizienten Verarbeitung unterschiedlicher Anfragetypen mit nur einem System, dass dieses System verschiedene Datenlayouts unterstützen muss. Dazu untersuchen wir in dieser Arbeit Möglichkeiten, um verschiedene Datenlayouts nachträglich in bestehende (und bekannte) Datenbanksysteme einzuschleusen. Das Ziel hierbei ist die Entwicklung einer flexiblen Speicherschicht, die verschiedenste Anfragen in einem einzigen Datenbanksystem unterstützen kann. Wir haben hierzu eine Reihe von nichtinvasiven Techniken, auch Trojanische Techniken genannt, entwickelt, mit denen sich verschiedene Datenlayouts nachträglich in existierende Systeme einschleusen lassen. Die Grundidee hinter diesen Trojanischen Techniken ist es, die Annahme, dass jedes Datenbanksystem nur eine festgelegte Art der Datenspeicherung haben kann, fallen zu lassen. Die Trojanischen Techniken erfordern nur minimale Änderungen am ursprünglichen Datenbanksystem, sondern beeinflussen dessen Verhalten von innen heraus. Der Einsatz Trojanischen Techniken kann die Anfragegeschwindigkeit erheblich steigern. Wir folgen mit diesem Ansatz einem Entwurfsmuster, das auch in anderen nichtinvasiven Forschungsprojekten wie PAX, fpB+-Bäume und RowCol verwendet wurde. Wir stellen in dieser Arbeit vier verschiedene Trojanische Techniken vor. Als erstes zeigen wir, wie Trojanische Indexe die Integration eines Index in Hadoop MapReduce ermöglichen. Ergänzt wird dies durch Trojanische Joins, welche kopartitionierte Joins in Hadoop MapReduce ermöglichen. Danach zeigen wir, wie Trojanische Layouts Hadoop MapReduce um zeilen-, spalten- und gruppierte spaltenorientierte Datenlayouts erweitern. Zusammen bilden diese Techniken eine flexible Speicherschicht für das Hadoop MapReduce Framework. Unsere vierte Technik, Trojanische Spalten, erlaubt es uns, spaltenorientierte Datenverarbeitung nachträglich in zeilenbasierten Datenbanksysteme einzuführen und lässt sich sogar auf kommerzielle closed-source Produkten anwenden. Wir schließen damit die Lücke zwischen zeilen- und spaltenorientierten Datenbanksystemen. In unseren Experimenten zeigen wir, dass die Trojanischen Techniken die Leistung des Hadoop MapReduce Frameworks um das bis zu 18fache und die Geschwindigkeit einer aktuellen kommerziellen Datenbank um das 17fache erhöhen können