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IP-based networks are prone to hardware failures, software errors and misconfigurations. This leads to service outages, such as those experienced by American Airlines in 2015. Moreover, cyber threats are becoming ever more sophisticated. As demonstrated by recent success stories of malware, such as the crimeware BlackEnergy, current defence solutions are insufficient to detect those anomalies and...
Today's dynamic computing deployment for commercial and scientific applications is propelling us to an era where minor inefficiencies can snowball into significant performance and operational bottlenecks. Data center operations is increasingly relying on sensors based control systems for key decision insights. The increased sampling frequencies, cheaper storage costs and prolific deployment of sensors...
This paper proposes a multi-level meta-classifier for identifying human activities based on accelerometer data. The training data consists of 77 subjects performing a combination of 23 different activities and monitored using a single hip-worn triaxial accelerometer. Time and frequency based features were extracted from two-second windows of raw accelerometer data and a subset of the features, together...
Although it is possible to design and manufacture MPSoCs with hundreds of processors, there is still a gap in the ability to debug hardware, software, and applications for such chips. Current state-of-the-art works related to MPSoC debugging suffer from poor integration, scalability in data storage, and simple graphical data representation. This work proposes a modular debugging framework to aid the...
Among many Big Data applications are those that deal with data streams. A data stream is a sequence of data points with timestamps that possesses the properties of transiency, infiniteness, uncertainty, concept drift, and multi-dimensionality. In this paper we propose an outlier detection technique called Orion that addresses all the characteristics of data streams. Orion looks for a projected dimension...
System event logs have been frequently used as a valuable resource in data-driven approaches to enhance system health and stability. A typical procedure in system log analytics is to first parse unstructured logs, and then apply data analysis on the resulting structured data. Previous work on parsing system event logs focused on offline, batch processing of raw log files. But increasingly, applications...
Monitoring high performance computing systems has become increasingly difficult as researchers and system analysts face the challenge of synthesizing a wide range of monitoring information in order to detect system problems on ever larger machines. We present a method for anomaly detection on syslog data, one of the most important data streams for determining system health. Syslog messages pose a...
In this paper, we present PaTSI, a tool for analyzing evolutions of objects in time series of satellite images. This tool is a plugin integrated in the KNIME Analytics Platform. PaTSI is a workflow composed of several nodes assembled together to form a whole KDD process (data selection, pre-processing, image segmentation, pattern mining and visualization). Input data consists of a time series of satellite...
This paper summarizes AAIA'16 Data Mining Challenge: Predicting Dangerous Seismic Events in Active Coal Mines which was held between October 5, 2015 and March 4, 2016 at the Knowledge Pit platform. It describes the scope and background of this competition and explains our research objectives which motivated the specific design of the competition rules. The paper also briefly overviews the results...
The work devoted to modeling graduates monitoring processes based on data mining, including a model of oscillations change the number of graduates from the profile employment and cluster analysis of the data by the method of k-means, using the contingency tables, the chi-square test and Spearman correlation coefficients. The practical part is implemented in Java object-oriented language in the NetBeans...
Today, third-party tracking services and passive traffic monitoring are extensively used to gather knowledge about users' internet activities and interests. Such tracking has significant privacy implications for the end users. This paper presents an overview of the third-party tracking usage. Using measurements, we highlight the current state of the third-party tracking landscape and differences observed...
We present a desktop monitoring application that combines keyboard, mouse, desktop and application-level activities. It has been developed to discover differences in cognitive functioning amongst older computer users indicative of mild cognitive impairment (MCI). Following requirements capture from clinical domain experts, the tool collects all Microsoft Windows events deemed potentially useful for...
In regard to distance learning, Brazil currently lives a very special moment, as many courses are being created within that type of education. Virtual Learning Environments (VLEs) have contributed to both the growth and the popularization of distance learning. The level of management of learning processes provided by VLEs, with great interaction and tracking quality, may be singled out as a key indicator...
In our case, an original television (TV) broadcasting monitoring system is proposed and then implemented in order to meet the urgent requirements for rapid development of radio and television industry. The monitoring system involves four primary sub-modules, the functions of which are respectively described as image extraction, network transmission and storage, dispatching and configuration, data...
This paper discusses the use of analytical software solutions in microfinance institutions and how it can improve the operations of the organization. The study has been conducted through a survey from MFIs located in various countries of Africa, Europe and Asia. The survey questions have been based on the kind of software solutions MFIs use and if they are able to benefit from it. Moreover, with this...
The explosion by amount of codes as well as the swelling logic complexity have stifled the performance of the traditional fault-location methods since the resource adopted during this process is unacceptable. Under such a situation, a scheme to locate the faults in complex software more effectively has been proposed in this paper based on networks community theory. First, on the base of establishing...
Traditional multi-step attack correlation approaches based on intrusion alerts face the challenge of recognizing attack scenarios because these approaches require complex pre-defined association rules as well as a high dependency on expert knowledge. Meanwhile, they barely consider the privacy issues. Under such circumstance, a novel algorithm is proposed to construct multi-step attack scenarios based...
An artificial Satellite's health and functioning in space is monitored by analysing the stream of Telemetry Data received at the Satellite Ground Control Centre. This data is received continually during the entire mission life of the Satellite. Hence a huge volume of Data is collected for any given satellite. Data Mining techniques are now regularly applied to analyse such large quantity of data for...
This paper introduces the reasons for big data analytics in distribution network studies and potential benefits it could give. Summary of the most common data mining methods used in power system studies is also given, followed by a comparative analysis. A use case is shown at the end in order to present some examples of extraction of useful information from raw data stored in a real distribution utility's...
Information systems are able to support the operational activities and provide useful information for users. In fact almost every large companies implementing information systems, but controlling the information system needs additional efforts, one of which is the audit of information systems. Audit is believed to be able to identify risks and evaluate the adequacy of the important controls on information...
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