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In this paper we consider the data caching problem in next generation data services in the cloud, which is characterized by using monetary cost and access trajectory information to control cache replacements, instead of exploiting capacityoriented strategies as in traditional research. In particular, given a stream of requests to a shared data item with respect to a homogeneous cost model, we first...
With the increasing popularity of serving and storing data in multiple data centers, we investigate the efficiency of majority quorum-based data consistency algorithms under this scenario. Because of the failure-prone nature of distributed storage systems, majority quorum-based data consistency algorithms become one of the most widely adopted approaches. In this paper, we propose the MeteorShower...
Finding the best model to reveal potential relationships of a given set of data is not an easy job and often requires many iterations of trial and errors for model sections, feature selections and parameters tuning. This problem is greatly complicated in the big data era where the I/O bottlenecks significantly slowed down the time needed to finding the best model. In this article, we examine the case...
With the arrival of big data era, cloud storage has become more ubiquitous. A growing number of consumers remote their data into cloud, as cloud can provide them with ample storage space and powerful computational capacity. However, storing data in cloud means that data is out of their control. How to verify the integrity of stored data and retrieve the corrupted data has become an urgent security...
We present a novel approach for detecting malicious user activity in databases. Specifically, we propose a new machine learning algorithm for detecting attacks such as a stolen user account or illegal use by a user. Our algorithm relies on two main components that examine the consistency of a user's activity and compare it with activity patterns learned from past access. The first component tests...
The main task of collision detection is to study whether multiple objects in a virtual scene contact or penetrate and response in time. And it occupies a decisive position in the current computer research field such as graphics, computer simulation and robot path planning, etc. In this paper, a quick collision detection algorithm based on cloud computing model is proposed by studying some related...
Implementing cloud computing empowers numerous paths to Web-based computing service offerings for meeting diverse needs. However, cloud data security and privacy information protection have also become a critical issue restraining the cloud applications. One of the major concerns in security is that cloud operators will have a chance to reach sensitive data, which dramatically increases users' anxiety...
Nowadays, data mining techniques are massively used via organizations intended for converting huge amount of data into information. Due to the advancement in database technology data are present at distributed sites consequently for carrying data mining analyzing in cost effective way we need to integrate these distributed data at one site. The predicament of anxiety here is that privacy of individual...
In this paper, we consider a mobile advertising recommendation system using item-based Top-N recommendation algorithm based on Hadoop framework and then propose a solution based on the Hadoop framework to build a distributed recommendation system. We also introduce advertising server into the system. Seller will pay for their advertising when it is clicked by users, so that this platform can make...
Binder, which helps to package the functional codes of system processes into inter-process invocable interfaces for application-level processes, is the core mechanism to implement the Inter-Process Communication(IPC) in Android. This paper, for the first time, attempts to study the system-level security properties of this mechanism. The universal injection interface and the model of IPC data are proposed...
The research and application of data warehouse and data mining technology is the core in this paper. The design and implementation of "The productive apparel sizes and styles auxiliary decision system" is the main line. The effect of productive sizes support system is to guide the sizes quantity and enhance production efficiency based on the result of sizes compression and sales trend analysis.
Multi-word Relevant Expressions (REs) can be defined as sequences of words (n grams) with strong semantic meaning, such as "ice melting" and "Ministère des Affaires Étrangères", useful in Information Retrieval, Document Clustering or Classification and Indexing of Documents. The need of extracting REs in several languages led research on statistical approaches rather than symbolic...
e-Mail is one of the primary mediums of electronic communication used today. The number of email accounts are expected to go over 4.3 billion accounts by the year 2016 according to the latest email statistic reports. Users receive a bulk of emails on a daily basis and consequently they tend to overlook their inboxes and miss important emails from important people. This email management issue imposes...
Massive cloud-based data-intensive applications (e.g., iterative MapReduce-based) could involve graph data processing. How to effectively analyze and process large-scale graph data is an unsolved challenging problem. We present a parallel computation framework, named MyBSP, which is inspired by Google's Pregel system. MyBSP supports and implements the Bulk Synchronous Parallel (BSP) programming model,...
How to effectively process massive graph data is an intractable challenging issue. In this paper, two types of parallel computation approaches were compared: MapReduce and MyBSP. MyBSP is our open source implementation which adopts the Bulk Synchronous Parallel (BSP) programming model to support iterative processing. The MapReduce-based and MyBSP-based PageRank algorithms were implemented respectively...
Deduplication is a commonly-used technique on disk-based storage pools. However, deduplication has not been used for tape-based pools: tape characteristics, such as high mount and seek times combined with data fragmentation resulting from deduplication create a toxic combination that leads to unacceptably high retrieval times. This work proposes DedupT, a system that efficiently supports deduplication...
With the increasingly growing amount of service requests from the world-wide customers, the cloud systems is capable of providing services while meeting the customers' satisfaction. Recently, to achieve the better reliability and performance, the cloud systems has been largely depending on the geographically distributed data centers. Nevertheless, the dollar cost of service placement by service providers...
The rapid growth of cloud computing as a newfound technology and many unclear security issues in it cause many challenges. These challenges are specified in service provider's cloud servers and transmission processes. Accordingly, this paper presents a model based on separate data and key cloud servers and a client-based data encryption service for increasing the reliability in cloud computing environments...
The problem of managing multidimensional stream cubes (i.e., data cubes originated from data streams) over Computational Grids still plays a critical role in Database and Data Warehousing research, since it covers a wide family of real-life application scenarios. Despite recent technological advancements, high dimensionality and massive size are still the most significant challenges to be addressed...
LeMo is the prototype of an application for learning analytics, which collects data about learners' activities from different learning platforms. The article describes design principles of LeMo and their implications for efficient learning analytics. Focus is on the LeMo system architecture, user path analysis by algorithms of sequential pattern mining, and visualization of learners' activities.
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