Large-Scale Spatial Data Management on Modern Parallel and Distributed Platforms

Rapidly growing volume of spatial data has made it desirable to develop efficient techniques for managing large-scale spatial data. Traditional spatial data management techniques cannot meet requirements of efficiency and scalability for large-scale spatial data processing. In this dissertation, we have developed new data-parallel designs for large-scale spatial data management that can better utilize modern inexpensive commodity parallel and distributed platforms, including multi-core CPUs, many-core GPUs and computer clusters, to achieve both efficiency and scalability. After introducing background on spatial data management and modern parallel and distributed systems, we present our parallel designs for spatial indexing and spatial join query processing on both multi-core CPUs and GPUs for high efficiency as well as their integrations with Big Data systems for better scalability. Experiment results using real world datasets demonstrate the effectiveness and efficiency of the proposed techniques on managing large-scale spatial data

Two-layer Space-oriented Partitioning for Non-point Data

Non-point spatial objects (e.g., polygons, linestrings, etc.) are ubiquitous. We study the problem of indexing non-point objects in memory for range queries and spatial intersection joins. We propose a secondary partitioning technique for space-oriented partitioning indices (e.g., grids), which improves their performance significantly, by avoiding the generation and elimination of duplicate results. Our approach is easy to implement and can be used by any space-partitioning index to significantly reduce the cost of range queries and intersection joins. In addition, the secondary partitions can be processed independently, which makes our method appropriate for distributed and parallel indexing. Experiments on real datasets confirm the advantage of our approach against alternative duplicate elimination techniques and data-oriented state-of-the-art spatial indices. We also show that our partitioning technique, paired with optimized partition-to-partition join algorithms, typically reduces the cost of spatial joins by around 50%.Comment: To appear in the IEEE Transactions on Knowledge and Data Engineerin

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

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

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

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

A systematic literature review of skyline query processing over data stream

Recently, skyline query processing over data stream has gained a lot of attention especially from the database community owing to its own unique challenges. Skyline queries aims at pruning a search space of a potential large multi-dimensional set of objects by keeping only those objects that are not worse than any other. Although an abundance of skyline query processing techniques have been proposed, there is a lack of a Systematic Literature Review (SLR) on current research works pertinent to skyline query processing over data stream. In regard to this, this paper provides a comparative study on the state-of-the-art approaches over the period between 2000 and 2022 with the main aim to help readers understand the key issues which are essential to consider in relation to processing skyline queries over streaming data. Seven digital databases were reviewed in accordance with the Preferred Reporting Items for Systematic Reviews (PRISMA) procedures. After applying both the inclusion and exclusion criteria, 23 primary papers were further examined. The results show that the identified skyline approaches are driven by the need to expedite the skyline query processing mainly due to the fact that data streams are time varying (time sensitive), continuous, real time, volatile, and unrepeatable. Although, these skyline approaches are tailored made for data stream with a common aim, their solutions vary to suit with the various aspects being considered, which include the type of skyline query, type of streaming data, type of sliding window, query processing technique, indexing technique as well as the data stream environment employed. In this paper, a comprehensive taxonomy is developed along with the key aspects of each reported approach, while several open issues and challenges related to the topic being reviewed are highlighted as recommendation for future research direction

Implementation and Evaluation of Algorithmic Skeletons: Parallelisation of Computer Algebra Algorithms

This thesis presents design and implementation approaches for the parallel algorithms of computer algebra. We use algorithmic skeletons and also further approaches, like data parallel arithmetic and actors. We have implemented skeletons for divide and conquer algorithms and some special parallel loops, that we call ‘repeated computation with a possibility of premature termination’. We introduce in this thesis a rational data parallel arithmetic. We focus on parallel symbolic computation algorithms, for these algorithms our arithmetic provides a generic parallelisation approach. The implementation is carried out in Eden, a parallel functional programming language based on Haskell. This choice enables us to encode both the skeletons and the programs in the same language. Moreover, it allows us to refrain from using two different languages—one for the implementation and one for the interface—for our implementation of computer algebra algorithms. Further, this thesis presents methods for evaluation and estimation of parallel execution times. We partition the parallel execution time into two components. One of them accounts for the quality of the parallelisation, we call it the ‘parallel penalty’. The other is the sequential execution time. For the estimation, we predict both components separately, using statistical methods. This enables very confident estimations, although using drastically less measurement points than other methods. We have applied both our evaluation and estimation approaches to the parallel programs presented in this thesis. We haven also used existing estimation methods. We developed divide and conquer skeletons for the implementation of fast parallel multiplication. We have implemented the Karatsuba algorithm, Strassen’s matrix multiplication algorithm and the fast Fourier transform. The latter was used to implement polynomial convolution that leads to a further fast multiplication algorithm. Specially for our implementation of Strassen algorithm we have designed and implemented a divide and conquer skeleton basing on actors. We have implemented the parallel fast Fourier transform, and not only did we use new divide and conquer skeletons, but also developed a map-and-transpose skeleton. It enables good parallelisation of the Fourier transform. The parallelisation of Karatsuba multiplication shows a very good performance. We have analysed the parallel penalty of our programs and compared it to the serial fraction—an approach, known from literature. We also performed execution time estimations of our divide and conquer programs. This thesis presents a parallel map+reduce skeleton scheme. It allows us to combine the usual parallel map skeletons, like parMap, farm, workpool, with a premature termination property. We use this to implement the so-called ‘parallel repeated computation’, a special form of a speculative parallel loop. We have implemented two probabilistic primality tests: the Rabin–Miller test and the Jacobi sum test. We parallelised both with our approach. We analysed the task distribution and stated the fitting configurations of the Jacobi sum test. We have shown formally that the Jacobi sum test can be implemented in parallel. Subsequently, we parallelised it, analysed the load balancing issues, and produced an optimisation. The latter enabled a good implementation, as verified using the parallel penalty. We have also estimated the performance of the tests for further input sizes and numbers of processing elements. Parallelisation of the Jacobi sum test and our generic parallelisation scheme for the repeated computation is our original contribution. The data parallel arithmetic was defined not only for integers, which is already known, but also for rationals. We handled the common factors of the numerator or denominator of the fraction with the modulus in a novel manner. This is required to obtain a true multiple-residue arithmetic, a novel result of our research. Using these mathematical advances, we have parallelised the determinant computation using the Gauß elimination. As always, we have performed task distribution analysis and estimation of the parallel execution time of our implementation. A similar computation in Maple emphasised the potential of our approach. Data parallel arithmetic enables parallelisation of entire classes of computer algebra algorithms. Summarising, this thesis presents and thoroughly evaluates new and existing design decisions for high-level parallelisations of computer algebra algorithms

JedAI-spatial: a system for 3-dimensional Geospatial Interlinking

Τα γεωχωρικά δεδομένα αποτελούν ένα σημαντικό κομμάτι των δεδομένων του Σημασιολογικού Ιστού (Semantic Web), αλλά μέχρι στιγμής οι πηγές του δεν περιέχουν αρκετούς συνδέσμους στο Linked Open Data cloud. Η Διασύνδεση Γεωχωρικών Δεδομένων (Geospatial Interlinking) έχει ως στόχο να καλύψει αυτό το κενό συνδέοντας τις γεωμετρίες με καθιερωμένες τοπολογικές σχέσεις, όπως αυτές του Dimensionally Extended 9-Intersection Model. Έχουν προταθεί διάφοροι αλγόριθμοι στη βιβλιογραφία για την επίλυση αυτού του προβληματος. Στο πλαίσιο αυτής της διπλωματικής εργασίας, αναπτύσσουμε το JedAI-spatial, ένα καινοτόμο σύστημα ανοιχτού κώδικα, το οποίο οργανώνει τους κύριους υπάρχοντες αλγορίθμους σύμφωνα με τρεις διαστάσεις: i. Το Space Tiling διαφοροποιεί τους αλγόριθμους διασύνδεσης σε αυτούς που βασίζονται σε πλέγμα (grid-based), δέντρα (tree-based) ή κατατμήσεις (partition-based), σύμφωνα με την μέθοδο τους για τη μείωση του χώρου αναζήτησης και συνεπώς της τετραγωνικής πολυπλοκότητας αυτού του προβλήματος. Η πρώτη κατηγορία περιέχει τεχνικές Σημασιολογικού Ιστού, η δεύτερη καθιερωμένες τεχνικές για χωρική διασύνδεση (spatial join) στην κύρια μνήμη από την κοινότητα των βάσεων δεδομένων , ενώ η τρίτη περιλαμβάνει παραλλαγές του βασικού αλγορίθμου plane-sweep της υπολογιστικής γεωμετρίας. ii. Το Budget awareness διαχωρίζει τους αλγόριθμους διασύνδεσης σε budget-agnostic και budget-aware. Οι μέν απαρτίζονται από batch τεχνικές, που παράγουν αποτελέσματα μόνο μετά την επεξεργασία όλων των δεδομένων, ενώ οι δε λειτουργούν με έναν προοδευτικό τρόπο που παράγει αποτελέσματα σταδιακά - ο στόχος τους είναι να επικυρώσουν τις τοπολογικά συσχετιζόμενες γεωμετρίες πριν από τις μη-συσχετιζόμενες. iii. Η Μέθοδος Εκτέλεσης διαφοροποιεί τους αλγορίθμους σε σειριακούς, οι οποίοι εκτελούνται χρησιμοποιώντας ένα πυρήνα (CPU core), και παράλληλους (parallel), οι οποίοι αξιοποιούν την κατανεμημένη εκτέλεση πάνω στο Apache Spark. Στα πλαίσια της διπλωματικής πραγματοποιήθηκαν εκτενή πειράματα με τις μεθόδους και των 3 διαστάσεων, με τα πειραματικά αποτελέσματα να παρέχουν μία ενδιαφέρουσα εικόνα όσον αφορά τη σχετική απόδοση των αλγορίθμων.Geospatial data constitutes a considerable part of Semantic Web data, but so far, its sources lack enough links in the Linked Open Data cloud. Geospatial Interlinking aims to cover this gap by associating geometries with established topological relations, such as those of the Dimensionally Extended 9-Intersection Model. Various algorithms have already been proposed in the literature for this task. In the context of this master thesis, we develop JedAI-spatial, a novel, open-source system that organizes the main existing algorithms according to three dimensions: i. Space Tiling distinguishes interlinking algorithms into grid-, tree- and partition-based, according to their approach for reducing the search space and, thus, the computational cost of this inherently quadratic task. The former category includes Semantic Web techniques that define a static or dynamic EquiGrid and verify pairs of geometries whose minimum bounding rectangles intersect at least one common cell. Tree-based algorithms encompass established main-memory spatial join techniques from the database community, while the partition-based category includes variations of the cornerstone of computational geometry, i.e., the plane sweep algorithm. ii. Budget-awareness distinguishes interlinking algorithms into budget-agnostic and budget-aware ones. The former constitute batch techniques that produce results only after completing their processing over the entire input data, while the latter operate in a pay-as-you-go manner that produces results progressively - their goal is to verify related geometries before the non-related ones. iii. Execution mode distinguishes interlinking algorithms into serial ones, which are carried out using a single CPU-core, and parallel ones, which leverage massive parallelization on top of Apache Spark. Extensive experimental evaluations were performed along these 3 dimensions, with the experimental outcomes providing interesting insights about the relative performance of the considered algorithms