Mobile Data Management

The management of data in the mobile computing environment offers new challenging problems. Existing software needs to be upgraded to accommodate this environment. To do so, the critical parameters need to be understood and defined. We have surveyed some problems and existing solution

Resource identification in fog-to-cloud systems: toward an identity management strategy

og-to-Cloud (F2C) is a novel paradigm aiming at extending the cloud computing capabilities to the edge of the network through the hierarchical and coordinated management of both, centralized cloud datacenters and distributed fog resources. It will allow all kinds of devices that are capable to connect to the F2C network to share its idle resources and access both, service provider and third parties’ resources to expand its own capabilities. However, despite the numerous advantages offered by the F2C model, such as the possibility of offloading delay-sensitive tasks to a nearby device and using the cloud infrastructure in the execution of resource-intensive tasks, the list of open challenges that needs to be addressed to have a deployable F2C system is pretty long. In this paper we focus on the resource identification challenge, proposing an identity management system (IDMS) solution that starts assigning identifiers (IDs) to the devices in the F2C network in a decentralized fashion using hashes and afterwards, manages the usage of those IDs applying a fragmentation technique. The obtained results during the validation phase show that our proposal not only meets the desired IDMS characteristics, but also that the fragmentation strategy is aligned with the constrained nature of the devices in the lowest tier of the network hierarchy.Peer ReviewedPostprint (author's final draft

Mobile Database System: Role of Mobility on the Query Processing

Abstract-The rapidly expanding technology of mobile communication will give mobile users capability of accessing information from anywhere and any time. The wireless technology has made it possible to achieve continuous connectivity in mobile environment. When the query is specified as continuous, the requesting mobile user can obtain continuously changing result. In order to provide accurate and timely outcome to requesting mobile user, the locations of moving object has to be closely monitored. The objective of paper is to discuss the problem related to the role of personal and terminal mobility and query processing in the mobile environment

Statistical Models for Querying and Managing Time-Series Data

In recent years we are experiencing a dramatic increase in the amount of available time-series data. Primary sources of time-series data are sensor networks, medical monitoring, financial applications, news feeds and social networking applications. Availability of large amount of time-series data calls for scalable data management techniques that enable efficient querying and analysis of such data in real-time and archival settings. Often the time-series data generated from sensors (environmental, RFID, GPS, etc.), are imprecise and uncertain in nature. Thus, it is necessary to characterize this uncertainty for producing clean answers. In this thesis we propose methods that address these important issues pertaining to time-series data. Particularly, this thesis is centered around the following three topics: Computing Statistical Measures on Large Time-Series Datasets. Computing statistical measures for large databases of time series is a fundamental primitive for querying and mining time-series data [31, 81, 97, 111, 132, 137]. This primitive is gaining importance with the increasing number and rapid growth of time-series databases. In Chapter 3, we introduce the Affinity framework for efficient computation of statistical measures by exploiting the concept of affine relationships [113, 114]. Affine relationships can be used to infer a large number of statistical measures for time series, from other related time series, instead of computing them directly; thus, reducing the overall computational cost significantly. Moreover, the Affinity framework proposes an unified approach for computing several statistical measures at once. Creating Probabilistic Databases from Imprecise Data. A large amount of time-series data produced in the real-world has an inherent element of uncertainty, arising due to the various sources of imprecision affecting its sources (like, sensor data, GPS trajectories, environmental monitoring data, etc.). The primary sources of imprecision in such data are: imprecise sensors, limited communication bandwidth, sensor failures, etc. Recently there has been an exponential rise in the number of such imprecise sensors, which has led to an explosion of imprecise data. Standard database techniques cannot be used to provide clean and consistent answers in such scenarios. Therefore, probabilistic databases that factor-in the inherent uncertainty and produce clean answers are required. An important assumption i while using probabilistic databases is that each data point has a probability distribution associated with it. This is not true in practice — the distributions are absent. As a solution to this fundamental limitation, in Chapter 4 we propose methods for inferring such probability distributions and using them for efficiently creating probabilistic databases [116]. Managing Participatory Sensing Data. Community-driven participatory sensing is a rapidly evolving paradigm in mobile geo-sensor networks. Here, sensors of various sorts (e.g., multi-sensor units monitoring air quality, cell phones, thermal watches, thermometers in vehicles, etc.) are carried by the community (public vehicles, private vehicles, or individuals) during their daily activities, collecting various types of data about their surrounding. Data generated by these devices is in large quantity, and geographically and temporally skewed. Therefore, it is important that systems designed for managing such data should be aware of these unique data characteristics. In Chapter 5, we propose the ConDense (Community-driven Sensing of the Environment) framework for managing and querying community-sensed data [5, 19, 115]. ConDense exploits spatial smoothness of environmental parameters (like, ambient pollution [5] or radiation [2]) to construct statistical models of the data. Since the number of constructed models is significantly smaller than the original data, we show that using our approach leads to dramatic increase in query processing efficiency [19, 115] and significantly reduces memory usage

Maintaining cache consistency in mobile computing environments.

by Leung Wing Man.Thesis (M.Phil.)--Chinese University of Hong Kong, 1996.Includes bibliographical references (leaves 73-75).Abstract --- p.iAcknowledgements --- p.iiiChapter 1 --- Introduction --- p.1Chapter 2 --- Background --- p.7Chapter 2.1 --- What is Mobile Computing? --- p.7Chapter 2.1.1 --- Applications of Mobile Computing --- p.8Chapter 2.1.2 --- New Challenges of Mobile Computing --- p.9Chapter 2.2 --- Related Work --- p.12Chapter 2.2.1 --- Lazy Replicated File Service --- p.12Chapter 2.2.2 --- Dividing the Database into Clusters --- p.14Chapter 2.2.3 --- Applying Causal Consistency --- p.15Chapter 2.3 --- Summary --- p.16Chapter 2.4 --- Serializability and Concurrency Control --- p.17Chapter 3 --- System Model and Suggested Protocol --- p.20Chapter 3.1 --- System Model --- p.20Chapter 3.2 --- Cache Management --- p.21Chapter 3.2.1 --- Version Control Mechanism --- p.22Chapter 3.2.2 --- Cache Consistency --- p.22Chapter 3.2.3 --- Request Data from Servers --- p.25Chapter 3.2.4 --- Invalidation Report --- p.27Chapter 3.2.5 --- Data Broadcasting --- p.30Chapter 4 --- Simulation Study --- p.32Chapter 4.1 --- Physical Queuing Model --- p.32Chapter 4.2 --- Logical System Model --- p.33Chapter 4.3 --- Parameter Setting --- p.34Chapter 4.4 --- The Significance of the Length of Invalidation Range --- p.37Chapter 4.4.1 --- Performance with Different Invalidation Range --- p.38Chapter 4.4.2 --- Increasing the Update Frequency --- p.40Chapter 4.4.3 --- Impact of Piggybacking Popular Data --- p.41Chapter 4.4.4 --- Increasing the Disconnection Period --- p.42Chapter 4.5 --- Comparison of the Proposed Protocol with the Amnesic Terminal Protocol --- p.44Chapter 4.5.1 --- Setting a Short Timeout Period --- p.45Chapter 4.5.2 --- Extending the Timeout Period --- p.46Chapter 4.5.3 --- Increasing the Frequency of Temporary Disconnection --- p.48Chapter 4.5.4 --- Increasing the Frequency of Crossing Boundaries --- p.49Chapter 4.6 --- Evaluate the Performance Gain with Piggybacking Message --- p.50Chapter 4.6.1 --- Adding Piggybacking Messages --- p.51Chapter 4.6.2 --- Reducing the Number of Popular Data --- p.52Chapter 4.6.3 --- Increasing the Frequency of Updates --- p.53Chapter 4.7 --- Behaviour of the Proposed Protocol --- p.54Chapter 4.7.1 --- Finding Maximum Number of Mobile Computers --- p.54Chapter 4.7.2 --- Interchanging the Frequency of Read-Only and Update Transactions --- p.55Chapter 5 --- Partially Replicated Database System --- p.57Chapter 5.1 --- Proposed Amendments --- p.57Chapter 5.1.1 --- Not Cache Partially Replicated Data ( Method 1 ) --- p.58Chapter 5.1.2 --- Drop Partially Replicated Data ( Method 2 ) --- p.59Chapter 5.1.3 --- Attaching Server-List ( Method 3 ) --- p.59Chapter 5.2 --- Experiments and Interpretation --- p.60Chapter 5.2.1 --- Partially Replicated Data with High Accessing Probability --- p.61Chapter 5.2.2 --- Reducing the Cache Size --- p.64Chapter 5.2.3 --- Partially Replicated Data with Low Accessing Probability --- p.65Chapter 6 --- Conclusions and Future Work --- p.70Chapter 6.1 --- Future Work --- p.72Bibliography --- p.73Chapter A --- Version Control Mechanism for Servers --- p.7

Ontology Based Data Access in Statoil

Ontology Based Data Access (OBDA) is a prominent approach to query databases which uses an ontology to expose data in a conceptually clear manner by abstracting away from the technical schema-level details of the underlying data. The ontology is ‘connected’ to the data via mappings that allow to automatically translate queries posed over the ontology into data-level queries that can be executed by the underlying database management system. Despite a lot of attention from the research community, there are still few instances of real world industrial use of OBDA systems. In this work we present data access challenges in the data-intensive petroleum company Statoil and our experience in addressing these challenges with OBDA technology. In particular, we have developed a deployment module to create ontologies and mappings from relational databases in a semi-automatic fashion; a query processing module to perform and optimise the process of translating ontological queries into data queries and their execution over either a single DB of federated DBs; and a query formulation module to support query construction for engineers with a limited IT background. Our modules have been integrated in one OBDA system, deployed at Statoil, integrated with Statoil’s infrastructure, and evaluated with Statoil’s engineers and data

QueueLinker: データストリームのための並列分散処理フレームワーク

早大学位記番号:新6373早稲田大

Issues on distributed caching of spatial data

Die Menge an digitalen Informationen über Orte hat bis heute rapide zugenommen. Mit der Verbreitung mobiler, internetfähiger Geräte kann nun jederzeit und von überall auf diese Informationen zugegriffen werden. Im Zuge dieser Entwicklung wurden zahlreiche ortsbasierte Anwendungen und Dienste populär. So reihen sich digitale Einkaufsassistenten und Touristeninformationsdienste sowie geosoziale Anwendungen in der Liste der beliebtesten Vertreter. Steigende Benutzerzahlen sowie die rapide wachsenden Datenmengen, stellen ernstzunehmende Herausforderungen für die Anbieter ortsbezogener Informationen dar. So muss der Datenbereitstellungsprozess effizient gestaltet sein, um einen kosteneffizienten Betrieb zu ermöglichen. Darüber hinaus sollten Ressourcen flexibel genug zugeordnet werden können, um Lastungleichgewichte zwischen Systemkomponenten ausgleichen zu können. Außerdem müssen Datenanbieter in der Lage sein, die Verarbeitungskapazitäten mit steigender und fallender Anfragelast zu skalieren. Mit dieser Arbeit stellen wir einen verteilten Zwischenspeicher für ortsbasierte Daten vor. In dem verteilten Zwischenspeicher werden Replika der am häufigsten verwendeten Daten von mehreren unabhängigen Servern im flüchtigen Speicher vorgehalten. Mit unserem Ansatz können die Herausforderungen für Anbieter ortsbezogener Informationen wie folgt addressiert werden: Zunächst sorgt eine speziell für die Zugriffsmuster ortsbezogener Anwendungen konzipierte Zwischenspreicherungsstragie für eine Erhöhung der Gesamteffizienz, da eine erhebliche Menge der zwischengespeicherten Ergebnisse vorheriger Anfragen wiederverwendet werden kann. Darüber hinaus bewirken unsere speziell für den Geo-Kontext entwickelten Lastbalancierungsverfahren den Ausgleich dynamischer Lastungleichgewichte. Letztlich befähigen unsere verteilten Protokolle zur Hinzu- und Wegnahme von Servern die Anbieter ortsbezogener Informationen, die Verarbeitungskapazität steigender oder fallender Anfragelast anzupassen. In diesem Dokument untersuchen wir zunächst die Anforderungen der Datenbereitstellung im Kontext von ortsbasierten Anwendungen. Anschließend diskutieren wir mögliche Entwurfsmuster und leiten eine Architektur für einen verteilten Zwischenspeicher ab. Im Verlauf dieser Arbeit, entstanden mehrere konkrete Implementierungsvarianten, die wir in diesem Dokument vorstellen und miteinander vergleichen. Unsere Evaluation zeigt nicht nur die prinzipielle Machbarkeit, sondern auch die Effektivität von unserem Caching-Ansatz für die Erreichung von Skalierbarkeit und Verfügbarkeit im Kontext der Bereitstellung von ortsbasierten Daten