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18th IEEE International Conference on Software Quality, Reliability, and Security (QRS)

作者： Morgado, Claudia Baioco, Gisele Busichia Basso, Tania Moraes, Regina Univ Estadual Campinas Limeira Brazil Ctr Res & Dev Telecommun Campinas Brazil

ISBN: (纸本)9781538677575

Nowadays, organizations collect vast amounts of data for future analysis. Motivated by this amount of data and requirements of Web2.0, a plethora of non-relational databases (nosql) emerged in recent years. However, several security features in relational databases (e.g., access control) have been left in non-relational management systems to be developed by the application, which can raise security breaches. This paper proposes a security model, based on the use of metadata, to provide access control for nosql graph-oriented database management system. The goal is to support the development of applications that use graph-oriented database in preserving the integrity of stored data and protect them from non-authorized access. A case study was performed as proof of concept, where the model was instantiated and implemented for Neo4j database. Results showed that access restrictions were applied correctly, avoiding unauthorized access. A schema for Neo4j was provided, once it does not have a native one.

关键词： Access Control Graph-oriented database nosql database Security

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Heterogeneous database System for Faster Data Querying using Elasticsearch 4

Heterogeneous Database System for Faster Data Querying using...

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4th International Conference on Computing Communication Control and Automation (ICCUBEA)

作者： Taware, Umesh Shaikh, Nuzhat MES Coll Engn Dept Comp Engn Pune Maharashtra India

ISBN: (纸本)9781538652572

Nowadays data is increasing at such high rate there is need of tool which can analyze this data in equally efficient matter, as this data analysis contributes to crucial decision making while dealing with day to day business scenarios. It is quite impractical to analyze this data with traditional relational database tools. There has been new growth in technologies which increases our ability to process to this data, Hadoop and nosql databases are some of the names that are very popular these for the same reason. In particular, Elasticsearch is most preferred tool now a days, it is text-based search engine developed by Apache Foundation. It is near real time, highly scalable and distributed tool to search, analyze and store your data. Hybrid database system proposes a solution to most common problem of increased data retrieval time as the size of data increases.

关键词： Elasticsearch Full-text Search nosql database Search Engine

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IoT Sensor Data Acquisition and Storage System Using Raspberry Pi and Apache Cassandra

IoT Sensor Data Acquisition and Storage System Using Raspber...

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IEEE International Conference and Workshop in Obuda on Electrical and Power Engineering (CANDO-EPE)

作者： Ferencz, Katalin Domokos, Jozsef Sapientia Hungarian Univ Transylvania Dept Elect Engn Targu Mures Romania

ISBN: (纸本)9781728111544

In this paper, we introduce an alternative solution to the many existing IoT data acquisition and storage systems. We present a self-designed and developed prototype electronic circuit extension for Raspberry Pi development board used for collecting sensor data. There is also presented a Pi4Java API based Java application used for sensor data collection and storage. We set up an Apache Cassandra database cluster, to stores large amounts of sensor data on lowcost servers, providing high availability. In addition, a web application is also presented, that allows different data visualization operations to be performed on the stored data. The presented system is a full IoT data acquisition, storage and visualization solution

关键词： sensor data acquisition data storage nosql database Pi4Java sensor data visualization

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Big Data Infrastructure, Data Visualisation and Challenges 2019

Big Data Infrastructure, Data Visualisation and Challenges

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3rd International Conference on Big Data and Internet of Things (BDIOT)

作者： Venkatraman, Ramanathan Venkatraman, Sitalakshmi Inst Syst Sci Singapore Singapore Melbourne Polytech Melbourne Vic Australia

ISBN: (纸本)9781450372466

The importance of Big Data is being realised worldwide with the advancement of information technologies, leveraging the capabilities of virtualization and cloud computing. Big Data infrastructure and the use of its tools and applications will significantly transform the data centers of businesses in the next decade. Data analytics is evolving with the new real-time capability of Big Data solutions to provide business intelligence for timely and effective decision making. However, Big Data poses various challenges related to the infrastructure and resource constraints, and other issues including security and privacy. This paper takes an initial step in recognizing the value of creating Big Data infrastructure for delivering high performance and scalable business intelligence in an organization. It presents the state-of-the-art tools and technologies for Big Data infrastructure and NIST framework. The advantages of data visualisation are illustrated thorough industry case scenarios. The Big Data trends and challenges are also discussed. Overall, this paper contributes to providing valuable insights unto the Big Data journey of an organization to enable a scalable infrastructure for achieving mission critical decision-making through data visualisation.

关键词： Big Data Big Data infrastructure cloud nosql database Hadoop data visualisation

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Research on performance testing model of databases of different structure

Research on performance testing model of databases of differ...

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2019 2nd International Conference on Mechanical,Electronic and Engineering Technology(MEET 2019)

作者： Liu Xiaochuang Institute of Network Technology Beijing University of Posts and Telecommunications

Using our self-innovate performance testing software PTF, Performance Testing Facility, this paper comes up with a new performance testing model and executes performance testing on many databases of different structure. The databases of different structure include HBase, MySQL, Oracle, DM7 and etc. After analyzing different features of these databases according to the result, we get the same standard as in the industry, which proves the usability of our model.

关键词： performance testing relational database nosql database HBase

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SD: A Divergence-Based Estimation Method for Service Demands in Cloud Systems

SD: A Divergence-Based Estimation Method for Service Demands...

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International Conference on Future Internet of Things and Cloud (FiCloud)

作者： Salvatore Dipietro Giuliano Casale Department of Computing Imperial College London United Kingdom

ISBN: (纸本)9781728128894

Estimating performance models parameters of cloud systems presents several challenges due to the distributed nature of the applications, the chains of interactions of requests with architectural nodes, and the parallelism and coordination mechanisms implemented within these systems. In this work, we present a new inference algorithm for model parameters, called state divergence (SD) algorithm, to accurately estimate resource demands in a complex cloud application. Differently from existing approaches, SD attempts to minimize the divergence between observed and modeled marginal state probabilities for individual nodes within an application, therefore requiring the availability of probabilistic measures from both the system and the underpinning model. Validation against a case study using the Apache Cassandra nosql database and random experiments show that SD can accurately predict demands and improve system behavior modeling and prediction.

关键词： Service demands inference queueing cloud nosql database Clouds Queue Inference algorithms Model checking Statistical analysis parallelism Legal Services

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Efficient Graph-based Author Disambiguation by Topological Similarity in DBLP 1

Efficient Graph-based Author Disambiguation by Topological S...

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1st IEEE International Conference on Artificial Intelligence and Knowledge Engineering (AIKE)

作者： Franzoni, Valentina Lepri, Michele Li, Yuanxi Milani, Alfredo Sapienza Univ Rome Dept Comp Control & Management Engn Rome Italy Engineers Profess Assoc Perugia Ordine Ingegneri Perugia Italy Hong Kong Baptist Univ Dept Comp Sci Hong Kong Peoples R China Univ Perugia Dept Math & Comp Sci Perugia Italy

ISBN: (纸本)9781538695555

In this work, we introduce a novel method for entity resolution author disambiguation in bibliographic networks. Such a method is based on a 2-steps network traversal using topological similarity measures for rating candidate nodes. Topological similarity is widely used in the Link Prediction application domain to assess the likelihood of an unknown link. A similarity function can be a good approximation for equality, therefore can be used to disambiguate, basing on the hypothesis that authors with many common co-authors are similar. Our method has experimented on a graph-based representation of the public DBLP Computer Science database. The results obtained are extremely encouraging regarding Precision, Accuracy, and Specificity. Further good aspects are the locality of the method for disambiguation assessment which avoids the need to know the global network, and the exploitation of only a few data, e.g. author name and paper title (i.e., co-authorship data).

关键词： disambiguation entity resolution semantic networks topological similarity link prediction bibliometrics graph-based database Neo4j nosql database co-authorship network social network analysis

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A New Method for Time-Series Big Data Effective Storage

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IEEE ACCESS 2017年 5卷 10694-10699页

作者： Tahmassebpour, Mahmoudreza Islamic Azad Univ North Tehran Branch Dept Informat Technol Engn Tehran *** Iran

Today, one of the main challenges of big data research is the processing of big time-series data. Moreover, time data analysis is of considerable importance, because previous trends are useful for predicting the future. Due to the considerable delay when the volume of the data increases, the presence of redundancy, and the innate lack of time-series structures, the traditional relational data model does not seem to be adequately able to analyze time data. Moreover, many traditional data structures do not support time operators, which results in an inefficient access to time data. Therefore, relational database management systems have difficulty in dealing with big data it may require massively parallel software that runs on many servers. This has led us to implement Chronos Software, an in-memory background-based time database for key-value pairs;this software was implemented using C++ language. An independent design has been suggested through appropriately using temporal algorithms, parallelism algorithms, and methods of data storage in RAM. Our results indicate that the employment of RAM for storing the data and of the Timeline Index algorithm for getting access to the time background of the keys in Chronos translate into an increase of about 40%-90% in the efficiency as compared with other databases, such as MySQL and MongoDB.

关键词： Timeline index nosql database Chronos database big data key-value database

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JOURNAL OF SYSTEMS AND SOFTWARE 2017年 127卷 28-51页

作者： Damaiyanti, Titus Irma Imawan, Ardi Indikawati, Fitri Indra Choi, Yoon-Ho Kwon, Joonho Pusan Natl Univ Sch Comp Sci & Engn Busan 46241 South Korea Pusan Natl Univ Dept Big Data Busan 46241 South Korea

Advancements in sensing and communication technologies are enabling intelligent transportation systems (ITS) to easily acquire large volumes of road traffic big data. Querying road traffic data is a crucial task for providing citizens with more insightful information on traffic conditions. In this paper, we have developed a similarity query system for road traffic big data, called SigTrac, that runs on top of an existing MongoDB document store. The SigTrac system represents road traffic sensor data having spatio-temporal characteristics into traffic matrices and stores them into a MongoDB nosql document store by exploiting map-reduce operations of MongoDB. In addition, SigTrac efficiently processes similarity queries for traffic data with singular value decomposition (SVD)-based and incremental SVD-based algorithms. Our experimental studies with real traffic data demonstrate the efficiency of SigTrac for similarity query processing for road traffic big data. (C) 2017 Elsevier Inc. All rights reserved.

关键词： Road traffic data Traffic matrices Similarity queries SVD-based query processing nosql database MongoDB

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An information modeling framework for bridge monitoring

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ADVANCES IN ENGINEERING SOFTWARE 2017年 114卷 11-31页

作者： Jeong, Seongwoon Hou, Rui Lynch, Jerome P. Sohn, Hoon Law, Kincho H. Stanford Univ Dept Civil & Environm Engn 473 Via Ortega Stanford CA 94305 USA Univ Michigan Dept Civil & Environm Engn 2350 Hayward St Ann Arbor MI 48109 USA Korea Adv Inst Sci & Technol Dept Civil & Environm Engn 291 Daehak Ro Daejeon 305701 South Korea

Bridge management involves a variety of information from different data sources, including geometric model, analysis model, bridge management system (BMS) and structural health monitoring (SHM) system. Current practice of bridge management typically handles these diverse types of data using isolated systems and operates with limited use of the data. Sharing and integration of such information would facilitate meaningful use of the information and improve bridge management, as well as enhance bridge operation and maintenance and public safety. In many industries, information models and interoperability standards have been developed and employed to facilitate information sharing and collaboration. Given the success of building information modeling (BIM) in the Architecture, Engineering and Construction (AEC) industry, efforts have been initiated to develop frameworks and standards for bridge information modeling (BrIM). Current developments of BrIM focus primarily on the physical descriptions of bridge structures, such as geometry and material properties. This paper presents an information modeling framework for supporting bridge monitoring applications. The framework augments and extends the prior work on the OpenBrIM standards to further capture the information relevant to engineering analysis and sensor network. Implementation of the framework employs an open-source nosql database system for scalability, flexibility and performance. The framework is demonstrated using bridge information and sensor data collected from the Telegraph Road Bridge located in Monroe, Michigan. The results show that the bridge information modeling framework can potentially facilitate the integration of information involved in bridge monitoring applications, and effectively support and provide services to retrieve and utilize the information. (C) 2017 Published by Elsevier Ltd.

关键词： Bridge information modeling Bridge management Bridge monitoring nosql database

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