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Emerging Big Data applications require a significant amount of server computational power. Big data analytics applications rely heavily on specific deep machine learning and data mining algorithms, and exhibit high computational intensity, memory intensity, I/O intensity and control intensity. Big data applications require computing resources that can efficiently scale to manage massive amounts of...
Botnets are considered one of the most dangerous species of network-based attack today because they involve the use of very large coordinated groups of hosts simultaneously. The behavioral analysis of computer networks is at the basis of the modern botnet detection methods, in order to intercept traffic generated by malwares for which signatures do not exist yet. Defining a pattern of features to...
Insider attack detection in an enterprise network environment is a critical problem that currently has no promising solution. It represents a significant challenge since host availability and performance requirements cannot be ignored. A network based approach allows these requirements to be met but is limited by the granularity of data available and the near impossibility of defining exact signatures...
Business critical web applications are the most popular services provided to the client by the financial sector. These applications are bringing handsome revenue for the financial industry every year. These services are also a frequent target of attackers. Poor coding practice leads applications to vulnerability that are exploited by attackers. Information and privileges such as access to databases,...
New Vacuum Solar Telescope (NVST) possesses a world-class solar telescope which observes the sun by high resolution image instruments and multi-wave spectrometer in visible and near infrared bands. NVST has gathered millions of Flexible Image Transport System (FITS) files since it has been for the routine observation and thousands of files are generated in a day. To search efficiently for a subset...
Mining global patterns on big data distributed in many remote locations is a challenging task since transmitting big data in different remote data servers to the central server is prohibitively expensive. In this paper, we present a succinct distributive big data clustering algorithm based on local-remote coordination (DBDC-LRC) that aims to reduce the cost of big data transmission while maintaining...
The analysis of Earth Observation data is a challenging task due to the variety, velocity and volume of incoming data from various sources. As storing all the raw data is almost impossible, knowledge extraction would be a recommended approach in reducing data size without losing valuable information. For describing the complex processing required to extract knowledge we propose a flexible solution...
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.
The boundary devices, such as routers, firewalls, proxies, and domain controllers, etc., are continuously generating logs showing the behaviors of the internal and external users, the working state of the network as well as the devices themselves. To rapidly and efficiently analyze these logs makes great sense in terms of security and reliability. However, it is a challenging task due to the fact...
DIBBs Brown Dog is a recent cyberinfrastructure effort which aims to create two new services to aid users in the searching, accessing, and usage of digital data and provide these services in a manner that is as broadly and easily accessible as possible. At its lowest level, the Data Access Proxy (DAP) providing file format conversion capabilities and the Data Tilling Service (DTS) providing content...
A content delivery network (CDN) using community information that is extracted from the data of a social networking service (SNS) is proposed in this paper. The structure of the load characteristics of the CDN in terms of the number of servers, communities, and downloads is also shown as a tool for evaluating network performance.
Social Network Analysis (SNA) is a field of study that focuses on analyzing user profiles and participations on social network channels in order to model relationships between people and to predict certain behaviors or knowledge. To achieve their goals, researchers, interested in SNA, have to extract content and structure from the numerous social networks available today. Existing tools, which help...
In modern VLSI designs, timing closure has become a challenging and tedious task for the designers due to the fact that the number of design logic gates led to an exponential increase in the number of timing paths. PrimeTime from Synopsys is used to perform Static Timing Analysis and to generate a timing report in the form of large size text file; hence, data analysis is a complex task for the designer...
Cloud computing has ushered a new interest in a paradigm called Datamining-as-a-service. This paradigm is aimed at organizations that lack the technical expertise or the computational resources enabling them to outsource their data mining tasks to a third party service provider. One of the main issues in this regard is the confidentiality of the valuable data at the server which the data owner considers...
With the rapid growth of the Internet speed and emergence of new applications, the amount of Internet traffic is continuously increasing. In order to provide stable Internet service, efficient network management based on accurate traffic identification is gaining much importance than ever. Header signature-based identification method for network management can be identified the network traffic quickly...
The rise of internet web-based application and smart phone ease access to the stock market, attracting newcomer investors. Most investors have made their decision based on information from stock investment researches published by the brokers. An automated stock recommendation system from stock investment research is introduced in this paper. The system collects several investment researches from multiple...
Preventing system failure in cloud has become more important as a result of the prevalence of cloud use for mission-critical applications. One of the major causes of system failure in clouds is misconfiguration, as shown in recent studies. Hence, it is essential first to detect misconfiguration before it causes outage or degradation of service. Although cloud provides us flexible and auto-configurable...
Air pollution poses an increased risk for respiratory infections and lung cancer. Monitoring systems on air pollution are common for outdoor environment. In this study, our focus is on the air monitoring in household environment and connects it to a personal health reporting system through a mobile APP. Data will be captured and stored in the cloud so as to improve computational efficiency and enhance...
A method of automatic extracting of temporal patterns from learning data for constructing hierarchical behavioral patterns based classifiers is considered in the paper. The presented approach can be used to complete the knowledge provided by experts or to discover the knowledge automatically if no expert knowledge is accessible. Formal description of temporal patterns is provided and an algorithm...
Reliably running large server infrastructures requires continuous monitoring of system states. Nowadays, these infrastructures are often being executed on an IaaS cloud infrastructure. At its heart IaaS cloud computing heavily relies on virtualization technology spanning from virtualized servers to networks. Compared to a typical server setup, virtualization introduces at least one additional layer...
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