IDEAS-1997-2021-Final-Programs

This document records the final program for each of the 26 meetings of the International Database and Engineering Application Symposium from 1997 through 2021. These meetings were organized in various locations on three continents. Most of the papers published during these years are in the digital libraries of IEEE(1997-2007) or ACM(2008-2021)

Voronoi classfied and clustered constellation data structure for three-dimensional urban buildings

In the past few years, the growth of urban area has been increasing and has resulted immense number of urban datasets. This situation contributes to the difficulties in handling and managing issues related to urban area. Huge and massive datasets can degrade the performance of data retrieval and information analysis. In addition, urban environments are very difficult to manage because they involved with various types of data, such as multiple types of zoning themes in urban mixeduse development. Thus, a special technique for efficient data handling and management is necessary. In this study, a new three-dimensional (3D) spatial access method, the Voronoi Classified and Clustered Data Constellation (VOR-CCDC) is introduced. The VOR-CCDC data structure operates on the basis of two filters, classification and clustering. To boost up the performance of data retrieval, VORCCDC offers a minimal percentage of overlap among nodes and a minimal coverage area in order to avoid repetitive data entry and multi-path queries. Besides that, VOR-CCDC data structure is supplemented with an extra element of nearest neighbour information. Encoded neighbouring information in the Voronoi diagram allows VOR-CCDC to optimally explore the data. There are three types of nearest neighbour queries that are presented in this study to verify the VOR-CCDC’s ability in finding the nearest neighbour information. The queries are Single Search Nearest Neighbour query, k Nearest Neighbour (kNN) query and Reverse k Nearest Neighbour (RkNN) query. Each query is tested with two types of 3D datasets; single layer and multi-layer. The test demonstrated that VOR-CCDC performs the least amount of input/output than their best competitor, the 3D R-Tree. Besides that, VOR-CCDC is also tested for performance evaluation. The results indicate that VOR-CCDC outperforms its competitor by responding 60 to 80 percent faster to the query operation. In the future, VOR-CCDC structure is expected to be expanded for temporal and dynamic objects. Besides that, VOR-CCDC structure can also be used in other applications such as brain cell database for analysing the spatial arrangement of neurons or analysing the protein chain reaction in bioinformatics applications

Complex queries and complex data

With the widespread availability of wearable computers, equipped with sensors such as GPS or cameras, and with the ubiquitous presence of micro-blogging platforms, social media sites and digital marketplaces, data can be collected and shared on a massive scale. A necessary building block for taking advantage from this vast amount of information are efficient and effective similarity search algorithms that are able to find objects in a database which are similar to a query object. Due to the general applicability of similarity search over different data types and applications, the formalization of this concept and the development of strategies for evaluating similarity queries has evolved to an important field of research in the database community, spatio-temporal database community, and others, such as information retrieval and computer vision. This thesis concentrates on a special instance of similarity queries, namely k-Nearest Neighbor (kNN) Queries and their close relative, Reverse k-Nearest Neighbor (RkNN) Queries. As a first contribution we provide an in-depth analysis of the RkNN join. While the problem of reverse nearest neighbor queries has received a vast amount of research interest, the problem of performing such queries in a bulk has not seen an in-depth analysis so far. We first formalize the RkNN join, identifying its monochromatic and bichromatic versions and their self-join variants. After pinpointing the monochromatic RkNN join as an important and interesting instance, we develop solutions for this class, including a self-pruning and a mutual pruning algorithm. We then evaluate these algorithms extensively on a variety of synthetic and real datasets. From this starting point of similarity queries on certain data we shift our focus to uncertain data, addressing nearest neighbor queries in uncertain spatio-temporal databases. Starting from the traditional definition of nearest neighbor queries and a data model for uncertain spatio-temporal data, we develop efficient query mechanisms that consider temporal dependencies during query evaluation. We define intuitive query semantics, aiming not only at returning the objects closest to the query but also their probability of being a nearest neighbor. After theoretically evaluating these query predicates we develop efficient querying algorithms for the proposed query predicates. Given the findings of this research on nearest neighbor queries, we extend these results to reverse nearest neighbor queries. Finally we address the problem of querying large datasets containing set-based objects, namely image databases, where images are represented by (multi-)sets of vectors and additional metadata describing the position of features in the image. We aim at reducing the number of kNN queries performed during query processing and evaluate a modified pipeline that aims at optimizing the query accuracy at a small number of kNN queries. Additionally, as feature representations in object recognition are moving more and more from the real-valued domain to the binary domain, we evaluate efficient indexing techniques for binary feature vectors.Nicht nur durch die Verbreitung von tragbaren Computern, die mit einer Vielzahl von Sensoren wie GPS oder Kameras ausgestattet sind, sondern auch durch die breite Nutzung von Microblogging-Plattformen, Social-Media Websites und digitale Marktplätze wie Amazon und Ebay wird durch die User eine gigantische Menge an Daten veröffentlicht. Um aus diesen Daten einen Mehrwert erzeugen zu können bedarf es effizienter und effektiver Algorithmen zur Ähnlichkeitssuche, die zu einem gegebenen Anfrageobjekt ähnliche Objekte in einer Datenbank identifiziert. Durch die Allgemeinheit dieses Konzeptes der Ähnlichkeit über unterschiedliche Datentypen und Anwendungen hinweg hat sich die Ähnlichkeitssuche zu einem wichtigen Forschungsfeld, nicht nur im Datenbankumfeld oder im Bereich raum-zeitlicher Datenbanken, sondern auch in anderen Forschungsgebieten wie dem Information Retrieval oder dem Maschinellen Sehen entwickelt. In der vorliegenden Arbeit beschäftigen wir uns mit einem speziellen Anfrageprädikat im Bereich der Ähnlichkeitsanfragen, mit k-nächste Nachbarn (kNN) Anfragen und ihrem Verwandten, den Revers k-nächsten Nachbarn (RkNN) Anfragen. In einem ersten Beitrag analysieren wir den RkNN Join. Obwohl das Problem von reverse nächsten Nachbar Anfragen in den letzten Jahren eine breite Aufmerksamkeit in der Forschungsgemeinschaft erfahren hat, wurde das Problem eine Menge von RkNN Anfragen gleichzeitig auszuführen nicht ausreichend analysiert. Aus diesem Grund formalisieren wir das Problem des RkNN Joins mit seinen monochromatischen und bichromatischen Varianten. Wir identifizieren den monochromatischen RkNN Join als einen wichtigen und interessanten Fall und entwickeln entsprechende Anfragealgorithmen. In einer detaillierten Evaluation vergleichen wir die ausgearbeiteten Verfahren auf einer Vielzahl von synthetischen und realen Datensätzen. Nach diesem Kapitel über Ähnlichkeitssuche auf sicheren Daten konzentrieren wir uns auf unsichere Daten, speziell im Bereich raum-zeitlicher Datenbanken. Ausgehend von der traditionellen Definition von Nachbarschaftsanfragen und einem Datenmodell für unsichere raum-zeitliche Daten entwickeln wir effiziente Anfrageverfahren, die zeitliche Abhängigkeiten bei der Anfragebearbeitung beachten. Zu diesem Zweck definieren wir Anfrageprädikate die nicht nur die Objekte zurückzugeben, die dem Anfrageobjekt am nächsten sind, sondern auch die Wahrscheinlichkeit mit der sie ein nächster Nachbar sind. Wir evaluieren die definierten Anfrageprädikate theoretisch und entwickeln effiziente Anfragestrategien, die eine Anfragebearbeitung zu vertretbaren Laufzeiten gewährleisten. Ausgehend von den Ergebnissen für Nachbarschaftsanfragen erweitern wir unsere Ergebnisse auf Reverse Nachbarschaftsanfragen. Zuletzt behandeln wir das Problem der Anfragebearbeitung bei Mengen-basierten Objekten, die zum Beispiel in Bilddatenbanken Verwendung finden: Oft werden Bilder durch eine Menge von Merkmalsvektoren und zusätzliche Metadaten (zum Beispiel die Position der Merkmale im Bild) dargestellt. Wir evaluieren eine modifizierte Pipeline, die darauf abzielt, die Anfragegenauigkeit bei einer kleinen Anzahl an kNN-Anfragen zu maximieren. Da reellwertige Merkmalsvektoren im Bereich der Objekterkennung immer öfter durch Bitvektoren ersetzt werden, die sich durch einen geringeren Speicherplatzbedarf und höhere Laufzeiteffizienz auszeichnen, evaluieren wir außerdem Indexierungsverfahren für Binärvektoren

Data centric storage framework for an intelligent wireless sensor network

In the last decade research into Wireless Sensor Networks (WSN) has triggered extensive growth in flexible and previously difficult to achieve scientific activities carried out in the most demanding and often remote areas of the world. This success has provoked research into new WSN related challenges including finding techniques for data management, analysis, and how to gather information from large, diverse, distributed and heterogeneous data sets. The shift in focus to research into a scalable, accessible and sustainable intelligent sensor networks reflects the ongoing improvements made in the design, development, deployment and operation of WSNs. However, one of the key and prime pre-requisites of an intelligent network is to have the ability of in-network data storage and processing which is referred to as Data Centric Storage (DCS). This research project has successfully proposed, developed and implemented a comprehensive DCS framework for WSN. Range query mechanism, similarity search, load balancing, multi-dimensional data search, as well as limited and constrained resources have driven the research focus. The architecture of the deployed network, referred to as Disk Based Data Centric Storage (DBDCS), was inspired by the magnetic disk storage platter consisting of tracks and sectors. The core contributions made in this research can be summarized as: a) An optimally synchronized routing algorithm, referred to Sector Based Distance (SBD) routing for the DBDCS architecture; b) DCS Metric based Similarity Searching (DCSMSS) with the realization of three exemplar queries – Range query, K-nearest neighbor query (KNN) and Skyline query; and c) A Decentralized Distributed Erasure Coding (DDEC) algorithm that achieves a similar level of reliability with less redundancy. SBD achieves high power efficiency whilst reducing updates and query traffic, end-to-end delay, and collisions. In order to guarantee reliability and minimizing end-to-end latency, a simple Grid Coloring Algorithm (GCA) is used to derive the time division multiple access (TDMA) schedules. The GCA uses a slot reuse concept to minimize the TDMA frame length. A performance evaluation was conducted with simulation results showing that SBD achieves a throughput enhancement by a factor of two, extension of network life time by 30%, and reduced end-to-end latency. DCSMSS takes advantage of a vector distance index, called iDistance, transforming the issue of similarity searching into the problem of an interval search in one dimension. DCSMSS balances the load across the network and provides efficient similarity searching in terms of three types of queries – range query, k-query and skyline query. Extensive simulation results reveal that DCSMSS is highly efficient and significantly outperforms previous approaches in processing similarity search queries. DDEC encoded the acquired information into n fragments and disseminated across n nodes inside a sector so that the original source packets can be recovered from any k surviving nodes. A lost fragment can also be regenerated from any d helper nodes. DDEC was evaluated against 3-Way Replication using different performance matrices. The results have highlighted that the use of erasure encoding in network storage can provide the desired level of data availability at a smaller memory overhead when compared to replication

Multimedia

The nowadays ubiquitous and effortless digital data capture and processing capabilities offered by the majority of devices, lead to an unprecedented penetration of multimedia content in our everyday life. To make the most of this phenomenon, the rapidly increasing volume and usage of digitised content requires constant re-evaluation and adaptation of multimedia methodologies, in order to meet the relentless change of requirements from both the user and system perspectives. Advances in Multimedia provides readers with an overview of the ever-growing field of multimedia by bringing together various research studies and surveys from different subfields that point out such important aspects. Some of the main topics that this book deals with include: multimedia management in peer-to-peer structures & wireless networks, security characteristics in multimedia, semantic gap bridging for multimedia content and novel multimedia applications

Tracking the Temporal-Evolution of Supernova Bubbles in Numerical Simulations

The study of low-dimensional, noisy manifolds embedded in a higher dimensional space has been extremely useful in many applications, from the chemical analysis of multi-phase flows to simulations of galactic mergers. Building a probabilistic model of the manifolds has helped in describing their essential properties and how they vary in space. However, when the manifold is evolving through time, a joint spatio-temporal modelling is needed, in order to fully comprehend its nature. We propose a first-order Markovian process that propagates the spatial probabilistic model of a manifold at fixed time, to its adjacent temporal stages. The proposed methodology is demonstrated using a particle simulation of an interacting dwarf galaxy to describe the evolution of a cavity generated by a Supernov

Enhancing quality of life in residential high-rises by sustainable design responses

After the end of World War II, in England was observed a proliferation of high-rises, replacing the destroyed buildings by the bombing and the existing slums. However, their popularity variated with time, at first being seen as a great improvement of the living condition, but later the authorities faced many complain that these buildings were unfit for normal living and especially for families and children. Nowadays England has to cope with a great heritage of towers from the 60s’ and the 70s’, as well as an increase in the construction of new high-rise buildings especially in the capital London. This research recognises the need to improve the design of the existing and future high-rises so they are in the same time sustainable and provide a better quality of life for the occupiers. Therefor an overall aim of the study was formulated: to investigate strengths, weaknesses, opportunities, and threats of the impact of high-rise buildings on quality of life through four objectives. The first one is to evaluate the connection between sustainability and quality of life indicators in high-rise buildings. The second one it to analyse liveability of high-rise buildings for better quality of life. The third one is to analyse energy-efficient solutions and their impact on enhancing quality of life. The final objective is to produce a conceptual framework for designing sustainable high-rise residential buildings enhancing quality of life. The methodology adopts an interpretive and realist paradigm and the data for this research was gathered from primary and secondary sources: interviews, observations and archival data. After searching SkyscraperPage database, it was found that two types of high-rise persist around UK and are numerous in London and Manchester: 12-16 storey buildings with brick cladding and 22-30 storey buildings with concrete cladding, so a limited number of buildings with these characteristics were selected for the case study. Actually, an effort to interview 3 to 5 residents in the same building at low, middle and high floors was made, instead of interviewing single residents from numerous different high-rises, in order to evaluate the significance of the height. The preferred method was interviews, as questionnaires were found to provide superficial data, open to misinterpretation from the researcher. Interviews, on the other hand, provided in-depth information about the social phenomenon: high-rise living, many surprising answers and most importantly, attitudes and preferences that were not influenced by the formulation of the questions but were formulated by the interviewees themselves. Seventeen interviews with residents were conducted in two old high-rises in Manchester and four in London to include the users ‘needs into the research process. Twelve interviews with residents in new high-rises illustrated the existing variations for the design issues. Additionally, twelve architects shared their proven experience designing residential high-rises, offering invaluable expertise for the design of sustainable residential high-rises providing better quality of life. The data generated then were analysed by content analysis and dynamic simulations with DesignBuilder of an approximated model of one of the studied high-rise complemented the interviews, investigating different building envelops and HVAC scenarios. This quantitative method supported the somewhat subjective findings regarding thermal comfort and cost-efficiency from the interviews. The main findings showed that specific sustainable design solutions are adequate for enhancing the quality of life in residential high-rise buildings. Some alerting data from the interviews with the residents reveals difficulties imposed by the design on the social interactions, more common in the old high-rises lacking any amenities. Other important findings inform that both existing and contemporary designs are not orientated towards the needs of the families with children, a major weakness stated by both the architects and the residents. The observations provided support for a view that both modern and old high-rises often fail to enhance and improve the built environment by both appearance and mix of functions, a valuable facet of the high-rises’ social impact. The analysis led to the elaboration of a theoretical framework that can be used by professionals and academics to guide their design process and constitutes the major contribution to knowledge