1,610 research outputs found
Towards trajectory anonymization: a generalization-based approach
Trajectory datasets are becoming popular due to the massive usage of GPS and locationbased services. In this paper, we address privacy issues regarding the identification of individuals in static trajectory datasets. We first adopt the notion of k-anonymity to trajectories and propose a novel generalization-based approach for anonymization of trajectories. We further show that releasing
anonymized trajectories may still have some privacy leaks. Therefore we propose a randomization based reconstruction algorithm for releasing anonymized trajectory data and also present how the underlying techniques can be adapted to other anonymity standards. The experimental results on real and synthetic trajectory datasets show the effectiveness of the proposed techniques
Cooperative scans
Data mining, information retrieval and other application areas exhibit a query load with multiple concurrent queries touching a large fraction of a relation. This leads to individual query plans based on a table scan or large index scan. The implementation of this access path in most database systems is straightforward. The Scan operator issues next page requests to the buffer manager without concern for the system state. Conversely, the buffer manager is not aware of the work ahead and it focuses on keeping the most-recently-used pages in the buffer pool. This paper introduces cooperative scans -- a new algorithm, based on a better sharing of knowledge and responsibility between the Scan operator and the buffer manager, which significantly improves performance of concurrent scan queries. In this approach, queries share the buffer content, and progress of the scans is optimized by the buffer manager by minimizing the number of disk transfers in light of the total workload ahead. The experimental results are based on a simulation of the various disk-access scheduling policies, and implementation of the cooperative scans within PostgreSQL and MonetDB/X100. These real-life experiments show that with a little effort the performance of existing database systems on concurrent scan queries can be strongly improve
Creating a Relational Distributed Object Store
In and of itself, data storage has apparent business utility. But when we can
convert data to information, the utility of stored data increases dramatically.
It is the layering of relation atop the data mass that is the engine for such
conversion. Frank relation amongst discrete objects sporadically ingested is
rare, making the process of synthesizing such relation all the more
challenging, but the challenge must be met if we are ever to see an equivalent
business value for unstructured data as we already have with structured data.
This paper describes a novel construct, referred to as a relational distributed
object store (RDOS), that seeks to solve the twin problems of how to
persistently and reliably store petabytes of unstructured data while
simultaneously creating and persisting relations amongst billions of objects.Comment: 12 pages, 5 figure
Consideration of interdependencies in the relational database system, and, A proposal and evaluation of an expert system for the relational database structure
This thesis addresses the issue of interdependencies in Distributed and non-Distributed Relational Database Management Systems and proposes a design and development of an Expert System to manage and enhance the current available Database Structures; In the first part, we study, compare and evaluate the interdependencies found in the operating environment relevant to the Distributed Relational structure. Hardware and software configurations are grouped and compared in an attempt to understand the interdependencies of the system so that an optimal configuration may be obtained; In the second part, we designed and developed an Expert System configuration with ease of use and functionality as foremost concerns. The system reuses the transient tables used to service queries to achieve a performance improvement without explicit user knowledge. Basic fragmentation principles are also used to aid in performance by implicitly restructuring the tables within a database to balance access time. (Abstract shortened with permission of author.)
Enabling Adaptive Grid Scheduling and Resource Management
Wider adoption of the Grid concept has led to an increasing amount of federated
computational, storage and visualisation resources being available to scientists and
researchers. Distributed and heterogeneous nature of these resources renders most of the
legacy cluster monitoring and management approaches inappropriate, and poses new
challenges in workflow scheduling on such systems. Effective resource utilisation monitoring
and highly granular yet adaptive measurements are prerequisites for a more efficient Grid
scheduler. We present a suite of measurement applications able to monitor per-process
resource utilisation, and a customisable tool for emulating observed utilisation models. We
also outline our future work on a predictive and probabilistic Grid scheduler. The research is
undertaken as part of UK e-Science EPSRC sponsored project SO-GRM (Self-Organising
Grid Resource Management) in cooperation with BT
- …