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.

Mobility-Induced Service Migration in Mobile Micro-clouds

Abstract: Mobile micro-cloud is an emerging technology in distributed computing, which is aimed at providing seamless computing/data access to the edge of the network when a centralized service may suffer from poor connectivity and long latency. Different from the traditional cloud, a mobile micro-cloud is smaller and deployed closer to users, typically attached to a cellular base station or wireless network access point. Due to the relatively small coverage area of each base station or access point, when a user moves across areas covered by different base stations or access points which are attached to different micro-clouds, issues of service performance and service migration become important. In this paper, we consider such migration issues. We model the general problem as a Markov decision process (MDP), and show that, in the special case where the mobile user follows a one-dimensional asymmetric random walk mobility model, the optimal policy for service migration is a threshold policy. We obtain the analytical solution for the cost resulting from arbitrary thresholds, and then propose an algorithm for finding the optimal thresholds. The proposed algorithm is more efficient than standard mechanisms for solving MDPs.

Data Sharing in the Post-Genomic World: The Experience of the International Cancer Genome Consortium (ICGC) Data Access Compliance Office (DACO)

Abstract: The scientific community, research funders, and governments have repeatedly recognized the importance of open access to genomic data for scientific research and medical progress [1]–[4]. Open access is becoming a well-established practice for large-scale, publicly funded, data-intensive community science projects, particularly in the field of genomics. Given this consensus, restrictions to open access should be regarded as exceptional and treated with caution. Yet, several developments [5] have led scientists and policymakers to investigate and implement open access restrictions [5]–[9]. Notably, there are privacy concerns within the genomics community and critiques from some researchers that open access, if left completely unregulated, could raise significant scientific, ethical, and legal issues (e.g., quality of the data, appropriate credit to data generators, relevance of the system for small and medium projects, etc.) [1]–[10]. A recent paper by Greenbaum and colleagues in this journal [11] identified protecting the privacy of study participants as the main challenge to open genomic data sharing. One possible way to reconcile open data sharing with privacy concerns is to use a tiered access system to separate access into “open” and “controlled.” Open access remains the norm for data that cannot be linked with other data to generate a dataset that would uniquely identify an individual. A controlled access mechanism, on the other hand, regulates access to certain, more sensitive data (e.g., detailed phenotype and outcome data, genome sequences files, raw genotype calls) by requiring third parties to apply to a body (e.g., custodian, original data collectors, independent body, or data access committee) and complete an access application that contains privacy safeguards. This mechanism, while primarily designed to protect study participants, can also be used to protect investigators, database hosting institutions, and funders from perceptions or acts of favoritism or impropriety. The experience of controlled access bodies to date has been only minimally documented in the literature [9], [12]. To address this lacuna, we present the experience of the Data Access Compliance Office (DACO) of the International Cancer Genome Consortium (ICGC). The goal is to provide information on this increasingly important type of database governance body.

Silverline: toward data confidentiality in storage-intensive cloud applications

Abstract: By offering high availability and elastic access to resources, third-party cloud infrastructures such as Amazon EC2 are revolutionizing the way today's businesses operate. Unfortunately, taking advantage of their benefits requires businesses to accept a number of serious risks to data security. Factors such as software bugs, operator errors and external attacks can all compromise the confidentiality of sensitive application data on external clouds, by making them vulnerable to unauthorized access by malicious parties. In this paper, we study and seek to improve the confidentiality of application data stored on third-party computing clouds. We propose to identify and encrypt all functionally encryptable data, sensitive data that can be encrypted without limiting the functionality of the application on the cloud. Such data would be stored on the cloud only in an encrypted form, accessible only to users with the correct keys, thus protecting its confidentiality against unintentional errors and attacks alike. We describe Silverline, a set of tools that automatically 1) identify all functionally encryptable data in a cloud application, 2) assign encryption keys to specific data subsets to minimize key management complexity while ensuring robustness to key compromise, and 3) provide transparent data access at the user device while preventing key compromise even from malicious clouds. Through experiments with real applications, we find that many web applications are dominated by storage and data sharing components that do not require interpreting raw data. Thus, Silverline can protect the vast majority of data on these applications, simplify key management, and protect against key compromise. Together, our techniques provide a substantial first step towards simplifying the complex process of incorporating data confidentiality into these storage-intensive cloud applications.

Methods and apparatus for providing host controlled caching of data in a storage system

Abstract: Mechanisms and techniques allow a host computer system to control caching operations within a data storage system. In a typical implementation, the system of the invention operates within a host computer system to intercept requests for access to data stored within a data storage system that originate from applications, programs or other processes that perform (e.g., execute) on the host computer system or another computer system. Once intercepted, the host computer can examine such a request for access to data to determine if the request matches any prefetch criteria defined within a prefetch database provided by the system of the invention. As an example, prefetch criteria defined in a prefetch database can specify various requestors (e.g., applications, programs, processes, users, or the like) that can access data (e.g., specific files, databases, volumes, data types or the like) stored within the data storage system according to various data access techniques (e.g., filesystems command such as open, read, write, seek and so forth). If a request for access to data matches prefetch criteria, the host computer configured with the invention determines prefetch information for related data related to data specified in the request based on the matching prefetch criteria. The prefetch information indicates the manner (e.g., amount, permanence, etc.) in which related data is to be cached in the data storage system. The host computer also obtains storage information for the related data that indicates the location of the related data stored within the data storage system. Using the prefetch information and the storage information, the invention generates one or more prefetch commands which indicate what related data is to be cached within the data storage system and from what storage locations within the data storage system that related data is to be obtained. The prefetch command is then transmitted to the data storage system to cause the data storage system to cache the related data.

Optique: Zooming in on Big Data

Abstract: Optique overcomes problems in current ontology-based data access systems pertaining to installation overhead, usability, scalability, and scope by integrating a user-oriented query interface, semi-automated managing methods, new query rewriting techniques, and temporal and streaming data processing in one platform.