M-Grid : A distributed framework for multidimensional indexing and querying of location based big data

The widespread use of mobile devices and the real time availability of user-location information is facilitating the development of new personalized, location-based applications and services (LBSs). Such applications require multi-attribute query processing, handling of high access scalability, support for millions of users, real time querying capability and analysis of large volumes of data. Cloud computing aided a new generation of distributed databases commonly known as key-value stores. Key-value stores were designed to extract value from very large volumes of data while being highly available, fault-tolerant and scalable, hence providing much needed features to support LBSs. However complex queries on multidimensional data cannot be processed efficiently as they do not provide means to access multiple attributes. In this thesis we present MGrid, a unifying indexing framework which enables key-value stores to support multidimensional queries. We organize a set of nodes in a P-Grid overlay network which provides fault-tolerance and efficient query processing. We use Hilbert Space Filling Curve based linearization technique which preserves the data locality to efficiently manage multi-dimensional data in a key-value store. We propose algorithms to dynamically process range and k nearest neighbor (kNN) queries on linearized values. This removes the overhead of maintaining a separate index table. Our approach is completely independent from the underlying storage layer and can be implemented on any cloud infrastructure. Experiments on Amazon EC2 show that MGrid achieves a performance improvement of three orders of magnitude in comparison to MapReduce and four times to that of MDHBase scheme --Abstract, pages iii-iv

Indexing RFID data using the VG-curve

Abstract Existing methods for the management of multidimensional data typically do not scale well with an increased number of dimensions or require the unsupported augmentation of the kernel. However, the use of multidimensional data continues to grow in modern database applications, specifically in spatio-temporal databases. These systems produce vast volumes of multidimensional data, and as such, data is stored in commercial RDBMS. Therefore, the efficient management of such multidimensional data is crucial. Despite it being applicable to any multidimensional vector data, we consider Radio Frequency Identifications (RFID) systems in this work. Due to RFID's acceptance and rapid growth into new and complex applications, together with the fact that, as with commercial applications, its data is stored within commercial RDBMS, we have chosen RFID as a pertinent testbed. We show that its data can be represented as vectors in multidimensional space and that the VG-curve combined with Multidimensional Dynamic Clustering Primary Index, which can be integrated into commercial RDBMS, can be used to efficiently access such data. In an empirical study conducted on three, five and nine dimensional RFID data we show that the presented concept outperforms available off-the-shelf options with a fraction of the required space

BLOCK: Efficient Execution of Spatial Range Queries in Main-Memory

The execution of spatial range queries is at the core of many applications, particularly in the simulation sciences but also in many other domains. Although main memory in desktop and supercomputers alike has grown considerably in recent years, most spatial indexes supporting the efficient execution of range queries are still only optimized for disk access (minimizing disk page reads). Recent research has primarily focused on the optimization of known disk-based approaches for memory (through cache alignment etc.) but has not fundamentally revisited index structures for memory. In this paper we develop BLOCK, a novel approach to execute range queries on spatial data featuring volumetric objects in main memory. Our approach is built on the key insight that in-memory approaches need to be optimized to reduce the number of intersection tests (between objects and query but also in the index structure). Our experimental results show that BLOCK outperforms known in-memory indexes as well as in-memory implementations of disk-based spatial indexes up to a factor of 7. The experiments show that it is more scalable than competing approaches as the data sets become denser

Topology-aware optimization of big sparse matrices and matrix multiplications on main-memory systems

Since data sizes of analytical applications are continuously growing, many data scientists are switching from customized micro-solutions to scalable alternatives, such as statistical and scientific databases. However, many algorithms in data mining and science are expressed in terms of linear algebra, which is barely supported by major database vendors and big data solutions. On the other side, conventional linear algebra algorithms and legacy matrix representations are often not suitable for very large matrices. We propose a strategy for large matrix processing on modern multicore systems that is based on a novel, adaptive tile matrix representation (AT MATRIX). Our solution utilizes multiple techniques inspired from database technology, such as multidimensional data partitioning, cardinality estimation, indexing, dynamic rewrites, and many more in order to optimize the execution time. Based thereon we present a matrix multiplication operator ATMULT, which outperforms alternative approaches. The aim of our solution is to overcome the burden for data scientists of selecting appropriate algorithms and matrix storage representations. We evaluated AT MATRIX together with ATMULT on several real-world and synthetic random matrices

A Survey on Array Storage, Query Languages, and Systems

Since scientific investigation is one of the most important providers of massive amounts of ordered data, there is a renewed interest in array data processing in the context of Big Data. To the best of our knowledge, a unified resource that summarizes and analyzes array processing research over its long existence is currently missing. In this survey, we provide a guide for past, present, and future research in array processing. The survey is organized along three main topics. Array storage discusses all the aspects related to array partitioning into chunks. The identification of a reduced set of array operators to form the foundation for an array query language is analyzed across multiple such proposals. Lastly, we survey real systems for array processing. The result is a thorough survey on array data storage and processing that should be consulted by anyone interested in this research topic, independent of experience level. The survey is not complete though. We greatly appreciate pointers towards any work we might have forgotten to mention.Comment: 44 page

LMSFC: A Novel Multidimensional Index based on Learned Monotonic Space Filling Curves

The recently proposed learned indexes have attracted much attention as they can adapt to the actual data and query distributions to attain better search efficiency. Based on this technique, several existing works build up indexes for multi-dimensional data and achieve improved query performance. A common paradigm of these works is to (i) map multi-dimensional data points to a one-dimensional space using a fixed space-filling curve (SFC) or its variant and (ii) then apply the learned indexing techniques. We notice that the first step typically uses a fixed SFC method, such as row-major order and z-order. It definitely limits the potential of learned multi-dimensional indexes to adapt variable data distributions via different query workloads. In this paper, we propose a novel idea of learning a space-filling curve that is carefully designed and actively optimized for efficient query processing. We also identify innovative offline and online optimization opportunities common to SFC-based learned indexes and offer optimal and/or heuristic solutions. Experimental results demonstrate that our proposed method, LMSFC, outperforms state-of-the-art non-learned or learned methods across three commonly used real-world datasets and diverse experimental settings.Comment: Extended Version. Accepted by VLDB 202

Efficient Processing of Range Queries in Main Memory

Datenbanksysteme verwenden Indexstrukturen, um Suchanfragen zu beschleunigen. Im Laufe der letzten Jahre haben Forscher verschiedene Ansätze zur Indexierung von Datenbanktabellen im Hauptspeicher entworfen. Hauptspeicherindexstrukturen versuchen möglichst häufig Daten zu verwenden, die bereits im Zwischenspeicher der CPU vorrätig sind, anstatt, wie bei traditionellen Datenbanksystemen, die Zugriffe auf den externen Speicher zu optimieren. Die meisten vorgeschlagenen Indexstrukturen für den Hauptspeicher beschränken sich jedoch auf Punktabfragen und vernachlässigen die ebenso wichtigen Bereichsabfragen, die in zahlreichen Anwendungen, wie in der Analyse von Genomdaten, Sensornetzwerken, oder analytischen Datenbanksystemen, zum Einsatz kommen. Diese Dissertation verfolgt als Hauptziel die Fähigkeiten von modernen Hauptspeicherdatenbanksystemen im Ausführen von Bereichsabfragen zu verbessern. Dazu schlagen wir zunächst die Cache-Sensitive Skip List, eine neue aktualisierbare Hauptspeicherindexstruktur, vor, die für die Zwischenspeicher moderner Prozessoren optimiert ist und das Ausführen von Bereichsabfragen auf einzelnen Datenbankspalten ermöglicht. Im zweiten Abschnitt analysieren wir die Performanz von multidimensionalen Bereichsabfragen auf modernen Serverarchitekturen, bei denen Daten im Hauptspeicher hinterlegt sind und Prozessoren über SIMD-Instruktionen und Multithreading verfügen. Um die Relevanz unserer Experimente für praktische Anwendungen zu erhöhen, schlagen wir zudem einen realistischen Benchmark für multidimensionale Bereichsabfragen vor, der auf echten Genomdaten ausgeführt wird. Im letzten Abschnitt der Dissertation präsentieren wir den BB-Tree als neue, hochperformante und speichereffziente Hauptspeicherindexstruktur. Der BB-Tree ermöglicht das Ausführen von multidimensionalen Bereichs- und Punktabfragen und verfügt über einen parallelen Suchoperator, der mehrere Threads verwenden kann, um die Performanz von Suchanfragen zu erhöhen.Database systems employ index structures as means to accelerate search queries. Over the last years, the research community has proposed many different in-memory approaches that optimize cache misses instead of disk I/O, as opposed to disk-based systems, and make use of the grown parallel capabilities of modern CPUs. However, these techniques mainly focus on single-key lookups, but neglect equally important range queries. Range queries are an ubiquitous operator in data management commonly used in numerous domains, such as genomic analysis, sensor networks, or online analytical processing. The main goal of this dissertation is thus to improve the capabilities of main-memory database systems with regard to executing range queries. To this end, we first propose a cache-optimized, updateable main-memory index structure, the cache-sensitive skip list, which targets the execution of range queries on single database columns. Second, we study the performance of multidimensional range queries on modern hardware, where data are stored in main memory and processors support SIMD instructions and multi-threading. We re-evaluate a previous rule of thumb suggesting that, on disk-based systems, scans outperform index structures for selectivities of approximately 15-20% or more. To increase the practical relevance of our analysis, we also contribute a novel benchmark consisting of several realistic multidimensional range queries applied to real- world genomic data. Third, based on the outcomes of our experimental analysis, we devise a novel, fast and space-effcient, main-memory based index structure, the BB- Tree, which supports multidimensional range and point queries and provides a parallel search operator that leverages the multi-threading capabilities of modern CPUs