scispace - formally typeset
Search or ask a question
Topic

Data access

About: Data access is a research topic. Over the lifetime, 13141 publications have been published within this topic receiving 172859 citations. The topic is also known as: Data access.


Papers
More filters
Patent
30 Oct 2015
TL;DR: In this article, the authors present a system for controlling the authentication or authorization of a mobile device user for enabling access to the resources or functionality associated with an application or service executable at the user's mobile device.
Abstract: Systems and methods are provided for controlling the authentication or authorization of a mobile device user for enabling access to the resources or functionality associated with an application or service executable at the user's mobile device. The user or user's mobile device may be automatically authenticated or authorized to access application or system resources at the device when the current geographic location of the user's mobile device is determined to be within a preauthorized zone, e.g., based on a predetermined geo-fence corresponding to the preauthorized zone. A security level or amount of authorization credentials required to authorize a user for data access may be varied according any of a plurality of security levels, when the current or last known geographic location of the user's mobile device is determined to be outside the preauthorized zone.

52 citations

01 Jan 2016
TL;DR: This work is the first to develop a secure k-NN classifier over encrypted data under the standard semi-honest model and empirically analyzes the efficiency of the solution through various experiments.
Abstract: 5 ABSTRACT: Data Mining has wide applications in many areas such as banking, medicine, scientific research and among government agencies. Classification is one of the commonly used tasks in data mining applications. For the past decade, due to the rise of various privacy issues, many theoretical and practical solutions to the classification problem have been proposed under different security models. However, with the recent popularity of cloud computing, users now have the opportunity to outsource their data, in encrypted form, as well as the data mining tasks to the cloud. Since the data on the cloud is in encrypted form, existing privacy preserving classification techniques are not applicable. In this paper, we focus on solving the classification problem over encrypted data. In particular, we propose a secure k-NN classifier over encrypted data in the cloud. The proposed k-NN protocol protects the confidentiality of the data, user's input query, and data access patterns. To the best of our knowledge, our work is the first to develop a secure k-NN classifier over encrypted data under the standard semi-honest model. Also, we empirically analyze the efficiency of our solution through various experiments.

52 citations

Journal ArticleDOI
TL;DR: GraphOne is designed and developed, a graph data store that abstracts thegraph data store away from the specialized systems to solve the fundamental research problems associated with the data store design and presents a new data abstraction, GraphView, to enable data access at two different granularities of data ingestions.
Abstract: There is a growing need to perform a diverse set of real-time analytics (batch and stream analytics) on evolving graphs to deliver the values of big data to users. The key requirement from such applications is to have a data store to support their diverse data access efficiently, while concurrently ingesting fine-grained updates at a high velocity. Unfortunately, current graph systems, either graph databases or analytics engines, are not designed to achieve high performance for both operations; rather, they excel in one area that keeps a private data store in a specialized way to favor their operations only. To address this challenge, we have designed and developed GraphOne, a graph data store that abstracts the graph data store away from the specialized systems to solve the fundamental research problems associated with the data store design. It combines two complementary graph storage formats (edge list and adjacency list) and uses dual versioning to decouple graph computations from updates. Importantly, it presents a new data abstraction, GraphView, to enable data access at two different granularities of data ingestions (called data visibility) for concurrent execution of diverse classes of real-time graph analytics with only a small data duplication. Experimental results show that GraphOne is able to deliver 11.40× and 5.36× average speedup in ingestion rate against LLAMA and Stinger, the two state-of-the-art dynamic graph systems, respectively. Further, they achieve an average speedup of 8.75× and 4.14× against LLAMA and 12.80× and 3.18× against Stinger for BFS and PageRank analytics (batch version), respectively. GraphOne also gains over 2,000× speedup against Kickstarter, a state-of-the-art stream analytics engine in ingesting the streaming edges and performing streaming BFS when treating first half as a base snapshot and rest as streaming edge in a synthetic graph. GraphOne also achieves an ingestion rate of two to three orders of magnitude higher than graph databases. Finally, we demonstrate that it is possible to run concurrent stream analytics from the same data store.

52 citations

Journal ArticleDOI
TL;DR: This paper focuses on the dynamic scheduling of query operators in the context of query scrambling, and shows that scrambling rescheduling is effective in hiding the impact of delays on query response time for a number of different delay scenarios.
Abstract: Distributed databases operating over wide-area networks such as the Internet, must deal with the unpredictable nature of the performance of communication. The response times of accessing remote sources can vary widely due to network congestion, link failure, and other problems. In such an unpredictable environment, the traditional iterator-based query execution model performs poorly. We have developed a class of methods, called query scrambling, for dealing explicitly with the problem of unpredictable response times. Query scrambling dynamically modifies query execution plans on-the-fly in reaction to unexpected delays in data access. In this paper we focus on the dynamic scheduling of query operators in the context of query scrambling. We explore various choices for dynamic scheduling and examine, through a detailed simulation, the effects of these choices. Our experimental environment considers pipelined and non-pipelined join processing in a client with multiple remote data sources and delayed or possibly bursty arrivals of data. Our performance results show that scrambling rescheduling is effective in hiding the impact of delays on query response time for a number of different delay scenarios.

52 citations

Posted Content
Tian Luo1, Rubao Lee1, Michael P. Mesnier2, Feng Chen2, Xiaodong Zhang1 
TL;DR: In this article, a heterogeneity-aware software framework for DBMS storage management called hStorage-DB is proposed, where semantic information that is critical for storage I/O is identified and passed to the storage manager.
Abstract: As storage systems become increasingly heterogeneous and complex, it adds burdens on DBAs, causing suboptimal performance even after a lot of human efforts have been made. In addition, existing monitoring-based storage management by access pattern detections has difficulties to handle workloads that are highly dynamic and concurrent. To achieve high performance by best utilizing heterogeneous storage devices, we have designed and implemented a heterogeneity-aware software framework for DBMS storage management called hStorage-DB, where semantic information that is critical for storage I/O is identified and passed to the storage manager. According to the collected semantic information, requests are classified into different types. Each type is assigned a proper QoS policy supported by the underlying storage system, so that every request will be served with a suitable storage device. With hStorage-DB, we can well utilize semantic information that cannot be detected through data access monitoring but is particularly important for a hybrid storage system. To show the effectiveness of hStorage-DB, we have implemented a system prototype that consists of an I/O request classification enabled DBMS, and a hybrid storage system that is organized into a two-level caching hierarchy. Our performance evaluation shows that hStorage-DB can automatically make proper decisions for data allocation in different storage devices and make substantial performance improvements in a cost-efficient way.

52 citations


Network Information
Related Topics (5)
Software
130.5K papers, 2M citations
86% related
Cloud computing
156.4K papers, 1.9M citations
86% related
Cluster analysis
146.5K papers, 2.9M citations
85% related
The Internet
213.2K papers, 3.8M citations
85% related
Information system
107.5K papers, 1.8M citations
83% related
Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202351
2022125
2021403
2020721
2019906
2018816