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Gossip-based service monitoring platform for wireless edge cloud computing

Gossip-based service monitoring platform for wireless edge c...

引用

IEEE International Conference on Networking, Sensing and Control

作者： Nuno Apolónia Felix Freitag Leandro Navarro Sarunas Girdzijauskas Vladimir Vlassov Universitat Politècnica de Catalunya Barcelona Spain KTH Royal Institute of Technology Stockholm Sweden

ISBN: (纸本)9781509044306

Edge cloud computing proposes to support shared services, by using the infrastructure at the network's edge. An important problem is the monitoring and management of services across the edge environment. Therefore, dissemination and gathering of data is not straightforward, differing from the classic cloud infrastructure. In this paper, we consider the environment of community networks for edge cloud computing, in which the monitoring of cloud services is required. We propose a monitoring platform to collect near real-time data about the services offered in the community network using a gossip-enabled network. We analyze and apply this gossip-enabled network to perform service discovery and information sharing, enabling data dissemination among the community. We implemented our solution as a prototype and used it for collecting service monitoring data from the real operational community network cloud, as a feasible deployment of our solution. By means of emulation and simulation we analyze in different scenarios, the behavior of the gossip overlay solution, and obtain average results regarding information propagation and consistency needs, i.e. in high latency situations, data convergence occurs within minutes.

关键词： Cloud computing Monitoring Peer-to-peer computing Wireless mesh networks Data dissemination distributed databases

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A Survey on Big Data in Financial Sector

A Survey on Big Data in Financial Sector

引用

International Conference on Networking and Network Applications (NaNA)

作者： Anku Jaiswal Purrushottam Bagale Department of Computer and Electronics Advanced College of Engineering and Management Lalitpur Nepal

With the advancement of computing technology, there has been rapid increase in volume, variety and velocity of data being produced, which is termed as Big Data. The Big data holds the potential to transform the enterprise operations and processes by leaps and bounds. One of the sectors that can highly benefit from the Big Data is financial. They have access to huge amount of transaction data which can be processed to gain the competitive advantages over their peers, enhance the customer banking experience, risk analysis and mitigation, operation and optimization. This paper discusses about the application of Big Data and its challenges in one of the financial sectors, which is Banking, and the benefit of Big Data in this field.

关键词： Big Data distributed databases Data mining Organizations Tools Banking

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Finding the Suitable Process Modeling for AIS Teaching: An Experimental Study

Finding the Suitable Process Modeling for AIS Teaching: An E...

引用

International Conference on Science in Information Technology

作者： Aisya Noor Husni Hamzah Ritchi Zaldy Adrianto Department of Accounting Universitas Padjadjaran Bandung Indonesia

Process modeling has long become an essential skill in Accounting Information System (AIS) assurance, analysis and design practice. As the result, many accounting curriculums have been mandating that process modeling languages and skills be introduced and taught to future system analysts accounting students. The purpose of this research is to empirically seek whether a suitable business process model can be used that is easily understood by users. Furthermore, the present study strives for potential contribution from itself whose result can serve as the input for teaching in information-system oriented subjects in accounting curriculum. To achieve the objective, comparison was made between UML 2.0 Activity Diagram with BPMN grammar to seek potential future adoption for better and understandable process modeling language. Meanwhile the earlier hypothesis was BMPN with its advantage on wider symbol number, the results showed that, despite showing only slight differences in results, UML 2.0 Activity Diagram can be more easily understood by the user compared with BPMN. Our findings suggest that the better teaching and contribution of process modeling could be addressed more by paying attention on aspects being external to the intrinsic features of modeling grammars.

关键词： Unified modeling language Business Process modeling Education distributed databases Information technology Information systems

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Team Based Learning in Computer Science Students

Team Based Learning in Computer Science Students

引用

International Conference on Science in Information Technology

作者： Brilly Andro Makalew Bens Pardamean Computer Science Department Bina Nusantara University Jakarta Indonesia

This research is an experiment on the implementation of Team Based Learning (TBL) in computer science students. 64 participants from Mobile Game Creative Design class was selected, and 3 variables are measured: motivation, engagement, academic achievement. The finding shows that although motivation and engagement are not affected, academic achievement shows a significant improvement with a pvalue of 0.0000271. This research serves as one of the basis to be considered when a university intends to implement TBL into its learning process.

关键词： distributed databases Computer science Education Information technology Atmospheric measurements Particle measurements Learning systems

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A distributed rough set theory based algorithm for an efficient big data pre-processing under the spark framework

A distributed rough set theory based algorithm for an effici...

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IEEE International Conference on Big Data

作者： Zaineb Chelly Dagdia Christine Zarges Gaël Beck Mustapha Lebbah Department of Computer Science Aberystywth University Aberystwyth United Kingdom Computer Science Laboratory (LIPN) University Paris-North-13 Villetaneuse France

Big Data reduction is a main point of interest across a wide variety of fields. This domain was further investigated when the difficulty in quickly acquiring the most useful information from the huge amount of data at hand was encountered. To achieve the task of data reduction, specifically feature selection, several state-of-the-art methods were proposed. However, most of them require additional information about the given data for thresholding, noise levels to be specified or they even need a feature ranking procedure. Thus, it seems necessary to think about a more adequate feature selection technique which can extract features using information contained within the dataset alone. Rough Set Theory (RST) can be used as such a technique to discover data dependencies and to reduce the number of features contained in a dataset using the data alone, requiring no additional information. However, despite being a powerful feature selection technique, RST is computationally expensive and only practical for small datasets. Therefore, in this paper, we present a novel efficient distributed Rough Set Theory based algorithm for large-scale data pre-processing under the Spark framework. Our experimental results show the efficient applicability of our RST solution to Big Data without any significant information loss.

关键词： Feature extraction Rough sets Partitioning algorithms Big Data Sparks distributed databases

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SARGS method for distributed actionable pattern mining using spark

SARGS method for distributed actionable pattern mining using...

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IEEE International Conference on Big Data

作者： Arunkumar Bagavathi Pranava Mummoju Katarzyna Tarnowska Angelina A. Tzacheva Zbigniew W. Ras Department of Computer Science University of North Carolina at Charlotte Charlotte NC USA

Actionability is a mode of revealing actionable knowledge in the form of Action Rules from large datasets. Action rule imparts in the form of recommendations as how a data object can change from one value to another more desirable value. The towering production of data in the recent years, due to increased usage of web, social media and IoT, has led to the age of big data. Also, abundant usage of cloud storages and cloud based services causes the data to be spread around the globe. This requires more time and space for a single computer to cope with such widespread data. Ecosystems like Hadoop MapReduce, Spark have been introduced to store, process and retrieve back the data efficiently in a distributed fashion. Data mining finds substantial improvements over such distributed frameworks to process huge volume of data and acquire knowledge from them in a short span of time. In this paper, we present an approach SARGS: Specific Action Rule discovery based on Grabbing Strategy, to build more specific Action Rules using Apache Spark framework and evaluate the results with our previous Hadoop MapReduce system (MR-Random Forest Algorithm for distributed Action Rules Discovery). Also, we propose a novel approach to distribute data in a distributed environment to get more optimal Action Rules and upgraded ARoGS algorithm to get more specific Action Rules.

关键词： Sparks Data mining Clustering algorithms distributed databases Machine learning algorithms Fault tolerance Fault tolerant systems

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A New Technique for File Carving on Hadoop Ecosystem

A New Technique for File Carving on Hadoop Ecosystem

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International Conference on New Trends in Computing Sciences (ICTCS)

作者： Esraa Alshammari Ghazi Al-Naymat Ali Hadi Department of Computer Science Princess Sumaya University for Technology Amman Jordan

ISBN: (纸本)9781538605288

using file carving techniques is one of most recent techniques that is used to retrieve the important data from unallocated space in a corrupted file system. In the traditional operating systems, such as Windows or Linux that have a small size of hard disk to store data, the researchers implemented many file carving techniques to carve a specific type of files (e.g. PDF, JPIG... etc.). However, with the presence of a specially designed file system that stores a huge volume of data, namely Hadoop distributed File System (HDFS), the carving techniques should be established to recover the minimum amount of data corrupted by attackers considering the HDFS capabilities. In this research, we propose a framework, which encompasses different steps working together as a new carving technique to retrieve the most possible pieces of files that are corrupted by 10%, and to ensure that the files are successfully carved.

关键词： File systems Transform coding Metadata distributed databases Ecosystems Digital forensics Data mining

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Exploring the consequences of distributed feature selection in DNA microarray data

Exploring the consequences of distributed feature selection ...

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International Joint Conference on Neural Networks

作者： Verónica Bolón-Canedo Konstantinos Sechidis Noelia Sánchez-Marono Amparo Alonso-Betanzos Gavin Brown Department of Computer Science University of A Coruna School of Computer Science University of Manchester

ISBN: (纸本)9781509061839

Microarray data classification has been typically seen as a difficult challenge for machine learning researchers mainly due to its high dimension in features while sample size is small. Because of this particularity, feature selection is usually applied trying to reduce its high dimensionality. However, existing algorithms may not scale well when dealing with this amount of features, and a possible solution is to distribute the features into several nodes. In this work we explore the process of distribution on microarray data-which has recently gained attention- and we evaluate to what extent it is possible to obtain similar results as those obtained with the whole dataset. We performed experiments with different aggregation methods, feature rankers and also evaluated the effect of distributing the feature ranking process in the subsequent classification performance.

关键词： DNA Redundancy distributed databases Algorithm design and analysis Computational modeling Gene expression Program processors

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Hybrid Approach with Zero Mean Distribution and Randomization for Privacy Preservation Technique

Hybrid Approach with Zero Mean Distribution and Randomizatio...

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International Conference on Innovations in Control, Communication and Information Systems (ICICCI)

作者： Mausumi Dey Anamika Ahirwar Dept. of Computer Science and Engineering Maharana Pratap College of Technology Gwalior India

ISBN: (纸本)9781538639412;9781538639405

Currently increasing in data exhaustive processing systems of information is now becomes very important for making decisions in the business or companies. Important and responsive knowledgeable designs may consist in process of the business analysis. Now a day privacy-preserving data-mining may obtain a huge enhancement with the help of data-mining and the researchers of the Information security community in the development of the technologies that are merges with the concerns of privacy. In the original data, the random-noise is added in the privacy preserving data Mining (PPDM) method, that is used for publishing the exact information regarding the actual data. The basic perspective of the privacy preserving data mining is to design various algorithms for updating the actual data and for providing the security to the information which is to be get misused, as a result of this the private data and private information still remain same even after the mining process. This paper calculates the results on comparative dependent analytical of the privacy preserving data mining algorithms and implemented those in MATLAB.

关键词： Data privacy Privacy Cryptography distributed databases Perturbation methods

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Tula: A disk latency aware balancing and block placement strategy for Hadoop

Tula: A disk latency aware balancing and block placement str...

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IEEE International Conference on Big Data

作者： Janakiram Dharanipragada Srikant Padala Balaji Kammili Vikram Kumar Dept. of Computer Science & Engineering IIT Madras Chennai India Center for Development of Advanced Computing Delhi India

Heterogeneity could occur due to various reasons in a Hadoop cluster. This work primarily focuses on heterogeneity occurring due to varying read/write latency of disks. In case of similar disk latencies among the datanodes, balancing the blocks uniformly is a suitable choice. However, with time, the disk latencies can increase due to mechanical problems and bad sectors. Further, the disks which crash and become non-functional are replaced with newer disks which could be of newer generation and can have greater RPM. This leads to heterogeneity in terms of disk in the cluster which is otherwise homogeneous, and balancing uniformly according to disk utilization may not give optimal job runtime. To address this issue we propose a disk latency aware balancer, which balances the cluster taking both disk latency and disk space utilization into consideration. This strategy for balancing makes sure that a low latency disk gets higher number of blocks in comparison to high latency disk. Furthermore, we introduce a custom block placement strategy considering disk latency and other factors. Our preliminary results show an improvement of upto 20% in job runtime.

关键词： Heuristic algorithms Clustering algorithms Kernel distributed databases Runtime Linux

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