Advances in Spatial and Temporal Databases, 11th International Symposium, SSTD 2009, Aalborg, Denmark, July 8-10, 2009, Proceedings

This volume constitutes the refereed proceedings of the 11th International Symposium on Spatial and Temporal Databases, SSTD 2009, held in Aalborg, Denmark, in July 2009. The 20 revised full papers presented together with 3 keynotes, 7 short papers, and 10 demonstration papers, were thoroughly reviewed and selected from a total of 62 research submissions and 11 demonstration submissions. The papers are organized in topical sections on spatial and flow networks, integrity and security, uncertain data and new technologies, indexing and monitoring moving objects, advanced queries, as well as on models and languages.link_to_subscribed_fulltex

Spatial Cloaking Revisited: Distinguishing Information Leakage from Anonymity

Abstract. Location-based services (LBS) are receiving increasing popularity as they provide convenience to mobile users with on-demand information. The use of these services, however, poses privacy issues as the user locations and queries are exposed to untrusted LBSs. Spatial cloaking techniques provide privacy in the form of k-anonymity; i.e., they guarantee that the (location of the) querying user u is indistinguishable from at least k-1 others, where k is a parameter specified by u at query time. To achieve this, they form a group of k users, including u, and forward their minimum bounding rectangle (termed anonymizing spatial region, ASR) to the LBS. The rationale behind sending an ASR instead of the distinct k locations is that exact user positions (querying or not) should not be disclosed to the LBS. This results in large ASRs with considerable dead-space, and leads to unnecessary performance degradation. Additionally, there is no guarantee regarding the amount of location information that is actually revealed to the LBS. In this paper, we introduce the concept of information leakage in spatial cloaking. We provide measures of this leakage, and show how we can trade it for better performance in a tunable manner. The proposed methodology directly applies to centralized and decentralized cloaking models, and is readily deployable on existing systems.

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Snapshot : friend or foe of data management - on optimizing transaction processing in database and blockchain systems

Data management is a complicated task. Due to a wide range of data management tasks, businesses often need a sophisticated data management infrastructure with a plethora of distinct systems to fulfill their requirements. Moreover, since snapshot is an essential ingredient in solving many data management tasks such as checkpointing and recovery, they have been widely exploited in almost all major data management systems that have appeared in recent years. However, snapshots do not always guarantee exceptional performance. In this dissertation, we will see two different faces of the snapshot, one where it has a tremendous positive impact on the performance and usability of the system, and another where an incorrect usage of the snapshot might have a significant negative impact on the performance of the system. This dissertation consists of three loosely-coupled parts that represent three distinct projects that emerged during this doctoral research. In the first part, we analyze the importance of utilizing snapshots in relational database systems. We identify the bottlenecks in state-of-the-art snapshotting algorithms, propose two snapshotting techniques, and optimize the multi-version concurrency control for handling hybrid workloads effectively. Our snapshotting algorithm is up to 100x faster and reduces the latency of analytical queries by up to 4x in comparison to the state-of-the-art techniques. In the second part, we recognize strict snapshotting used by Fabric as a critical bottleneck, and replace it with MVCC and propose some additional optimizations to improve the throughput of the permissioned-blockchain system by up to 12x under highly contended workloads. In the last part, we propose ChainifyDB, a platform that transforms an existing database infrastructure into a blockchain infrastructure. ChainifyDB achieves up to 6x higher throughput in comparison to another state-of-the-art permissioned blockchain system. Furthermore, its external concurrency control protocol outperforms the internal concurrency control protocol of PostgreSQL and MySQL, achieving up to 2.6x higher throughput in a blockchain setup in comparison to a standalone isolated setup. We also utilize snapshots in ChainifyDB to support recovery, which has been missing so far from the permissioned-blockchain world.Datenverwaltung ist eine komplizierte Aufgabe. Aufgrund der vielfältigen Aufgaben im Bereich der Datenverwaltung benötigen Unternehmen häufig eine anspruchsvolle Infrastruktur mit einer Vielzahl an unterschiedlichen Systemen, um ihre Anforderungen zu erfüllen. Dabei ist Snapshotting ein wesentlicher Bestandteil in nahezu allen aktuellen Datenbanksystemen, um Probleme wie Checkpointing und Recovery zu lösen. Allerdings garantieren Snapshots nicht immer eine gute Performance. In dieser Arbeit werden wir zwei Facetten des Snapshots beleuchten: Einerseits können Snapshots enorm positive Auswirkungen auf die Performance und Usability des Systems haben, andererseits können sie bei falscher Anwendung zu erheblichen Performanceverlusten führen. Diese Dissertation besteht aus drei Teilen basierend auf drei unterschiedlichen Projekten, die im Rahmen der Forschung zu dieser Arbeit entstanden sind. Im ersten Teil untersuchen wir die Bedeutung von Snapshots in relationalen Datenbanksystemen. Wir identifizieren die Bottlenecks gegenwärtiger Snapshottingalgorithmen, stellen zwei leichtgewichtige Snapshottingverfahren vor und optimieren Multi- Version Concurrency Control f¨ur das effiziente Ausführen hybrider Workloads. Unser Snapshottingalgorithmus ist bis zu 100 mal schneller und verringert die Latenz analytischer Anfragen um bis zu Faktor vier gegenüber dem Stand der Technik. Im zweiten Teil identifizieren wir striktes Snapshotting als Bottleneck von Fabric. In Folge dessen ersetzen wir es durch MVCC und schlagen weitere Optimierungen vor, mit denen der Durchsatz des Permissioned Blockchain Systems unter hoher Arbeitslast um Faktor zwölf verbessert werden kann. Im letzten Teil stellen wir ChainifyDB vor, eine Platform die eine existierende Datenbankinfrastruktur in eine Blockchaininfrastruktur überführt. ChainifyDB erreicht dabei einen bis zu sechs mal höheren Durchsatz im Vergleich zu anderen aktuellen Systemen, die auf Permissioned Blockchains basieren. Das externe Concurrency Protokoll übertrifft dabei sogar die internen Varianten von PostgreSQL und MySQL und erreicht einen bis zu 2,6 mal höhren Durchsatz im Blockchain Setup als in einem eigenständigen isolierten Setup. Zusätzlich verwenden wir Snapshots in ChainifyDB zur Unterstützung von Recovery, was bisher im Rahmen von Permissioned Blockchains nicht möglich war

Geographical places as a personalisation element: extracting profiles from human activities and services of visited places in mobility logs

Collecting personal mobility traces of individuals is currently applicable on a large scale due to the popularity of position-aware mobile phones. Statistical analysis of GPS data streams, collected with a mobile phone, can reveal several interesting measures such as the most frequently visited geographical places by some individual. Applying probabilistic models to such data sets can predict the next place to visit, and when. Several practical applications can utilise the results of such analysis. Current state of the art, however, is limited in terms of the qualitative analysis of personal mobility logs. Without explicit user-interactions, not much semantics can be inferred from a GPS log. This work proposes the utilisation of the common human activities and services provided at certain place types to extract semantically rich profiles from personal mobility logs. The resulting profiles include spatial, temporal and generic thematic description of a user. The work introduces several pre-processing methods for GPS data streams, collected with personal mobile devices, which improved the quality of the place extraction process from GPS logs. The thesis also introduces a method for extracting place semantics from multiple data sources. A textual corpus of functional descriptions of human activities and services associated with certain geographic place types is analysed to identify the frequent linguistic patterns used to describe such terms. The patterns found are then matched against multiple textual data sources of place semantics, to extract such terms, for a collection of place types. The results were evaluated in comparison to an equivalent expert ontology, as well as to semantics collected from the general public. Finally, the work proposes a model for the resulting profiles, the necessary algorithms to build and utilise such profiles, along with an encoding mark-up language. A simulated mobile application was developed to show the usability and for evaluation of the resulting profiles