scholarly journals STLIS: A Scalable Two-Level Index Scheme for Big Data in IoT

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Yonglin Leng ◽  
Zhikui Chen ◽  
Yueming Hu
Keyword(s):  
Big Data ◽  
Large Scale ◽  
Rapid Development ◽  
Storage Space ◽  
Important Challenge ◽  
Rdf Data ◽  
The Internet Of Things ◽  
Dramatic Growth ◽  
High Storage ◽  
Effective Representation

The rapid development of the Internet of Things causes the dramatic growth of data, which poses an important challenge on the storage and quick retrieval of big data. As an effective representation model, RDF receives the most attention. More and more storage and index schemes have been developed for RDF model. For the large-scale RDF data, most of them suffer from a large number of self-joins, high storage cost, and many intermediate results. In this paper, we propose a scalable two-level index scheme (STLIS) for RDF data. In the first level, we devise a compressed path template tree (CPTT) index based on S-tree to retrieve the candidate sets of full path. In the second level, we create a hierarchical edge index (HEI) and a node-predicate (NP) index to accelerate the match. Extensive experiments are executed on two representative RDF benchmarks and one real RDF dataset in IoT by comparison with three representative index schemes, that is, RDF-3X, Bitmat, and TripleBit. Results demonstrate that our proposed scheme can respond to the complex query in real time and save much storage space compared with RDF-3X and Bitmat.

Efficient Indexing RDF Query Algorithm for Big Data

2013 ◽  
Vol 441 ◽  
pp. 691-694
Author(s):  
Yi Qun Zeng ◽  
Jing Bin Wang
Keyword(s):  
Large Scale ◽  
Rapid Development ◽  
Large Data ◽  
Index Structure ◽  
Data Query ◽  
Large Scale Data ◽  
Tree Index ◽  
Rdf Data ◽  
Query Algorithm ◽  
Scale Data

With the rapid development of information technology, data grows explosionly, how to deal with the large scale data become more and more important. Based on the characteristics of RDF data, we propose to compress RDF data. We construct an index structure called PAR-Tree Index, then base on the MapReduce parallel computing framework and the PAR-Tree Index to execute the query. Experimental results show that the algorithm can improve the efficiency of large data query.

scholarly journals Military Simulation Big Data: Background, State of the Art, and Challenges

2015 ◽  
Vol 2015 ◽  
pp. 1-20 ◽  
Author(s):  
Xiao Song ◽  
Yulin Wu ◽  
Yaofei Ma ◽  
Yong Cui ◽  
Guanghong Gong
Keyword(s):  
Big Data ◽  
Large Scale ◽  
Rapid Development ◽  
Military Training ◽  
Great Success ◽  
Service Oriented Architectures ◽  
Weapon Systems ◽  
Service Oriented ◽  
High Resolution Models ◽  
Future Work

Big data technology has undergone rapid development and attained great success in the business field. Military simulation (MS) is another application domain producing massive datasets created by high-resolution models and large-scale simulations. It is used to study complicated problems such as weapon systems acquisition, combat analysis, and military training. This paper firstly reviewed several large-scale military simulations producing big data (MS big data) for a variety of usages and summarized the main characteristics of result data. Then we looked at the technical details involving the generation, collection, processing, and analysis of MS big data. Two frameworks were also surveyed to trace the development of the underlying software platform. Finally, we identified some key challenges and proposed a framework as a basis for future work. This framework considered both the simulation and big data management at the same time based on layered and service oriented architectures. The objective of this review is to help interested researchers learn the key points of MS big data and provide references for tackling the big data problem and performing further research.

Research on Micro-Certificate Based Security System for Internet of Things

2012 ◽  
Vol 263-266 ◽  
pp. 3125-3129
Author(s):  
Li Ping Du ◽  
Ying Li ◽  
Guan Ning Xu ◽  
Fei Duan
Keyword(s):  
Internet Of Things ◽  
Large Scale ◽  
Rapid Development ◽  
Security System ◽  
Security Requirements ◽  
Full Account ◽  
Chip Technology ◽  
Cryptographic Algorithms ◽  
Encryption Decryption ◽  
The Internet Of Things

The rapid development of internet of things puts forward urgent needs for security. The security system must be studied to adapt to the characteristics of the internet of things. The micro- certificate based security system for internet of things takes full account of the security characteristics of things, and uses the symmetric cryptographic algorithms and security chip technology. This security system can meet the security requirements for large-scale sensor’s authentication, signification and encryption/decryption in internet of things, and improve the security performance of internet of things greatly.

scholarly journals Evaluation of big data frameworks for analysis of smart grids

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Mohammad Hasan Ansari ◽  
Vahid Tabatab Vakili ◽  
Behnam Bahrak
Keyword(s):  
Big Data ◽  
Smart Grid ◽  
Cyber Security ◽  
Smart Grids ◽  
Large Scale ◽  
Rapid Development ◽  
Massive Data ◽  
Grid Data ◽  
Data Generator ◽  
And Performance

AbstractWith the rapid development of smart grids and increasing data collected in these networks, analyzing this massive data for applications such as marketing, cyber-security, and performance analysis, has gained popularity. This paper focuses on analysis and performance evaluation of big data frameworks that are proposed for handling smart grid data. Since obtaining large amounts of smart grid data is difficult due to privacy concerns, we propose and implement a large scale smart grid data generator to produce massive data under conditions similar to those in real smart grids. We use four open source big data frameworks namely Hadoop-Hbase, Cassandra, Elasticsearch, and MongoDB, in our implementation. Finally, we evaluate the performance of different frameworks on smart grid big data and present a performance benchmark that includes common data analysis techniques on smart grid data.

Application research of urban subway traffic mode based on behavior entropy in the background of big data

2021 ◽  
pp. 1-14
Author(s):  
Wanxin Hu ◽  
Fen Cheng
Keyword(s):  
Big Data ◽  
Large Scale ◽  
Transportation Networks ◽  
Transportation Network ◽  
Rapid Development ◽  
Urban Transportation ◽  
Transportation System ◽  
Urban Traffic ◽  
Time Optimization ◽  
The City

With the development of society and the Internet and the advent of the cloud era, people began to pay attention to big data. The background of big data brings opportunities and challenges to the research of urban intelligent transportation networks. Urban transportation system is one of the important foundations for maintaining urban operation. The rapid development of the city has brought tremendous pressure on the traffic, and the congestion of urban traffic has restricted the healthy development of the city. Therefore, how to improve the urban transportation network model and improve transportation and transportation has become an urgent problem to be solved in urban development. Specific patterns hidden in large-scale crowd movements can be studied through transportation networks such as subway networks to explore urban subway transportation modes to support corresponding decisions in urban planning, transportation planning, public health, social networks, and so on. Research on urban subway traffic patterns is crucial. At the same time, a correct understanding of the behavior patterns and laws of residents’ travel is a key factor in solving urban traffic problems. Therefore, this paper takes the metro operation big data as the background, takes the passenger travel behavior in the urban subway transportation system as the research object, uses the behavior entropy to measure the human behavior, and actively explores the urban subway traffic mode based on the metro passenger behavior entropy in the context of big data. At the same time, the congestion degree of the subway station is analyzed, and the redundancy time optimization model of the subway train stop is established to improve the efficiency of the subway operation, so as to provide important and objective data and theoretical support for the traveler, planner and decision maker. Compared to the operation graph without redundant time, the total travel time optimization effect of passengers is 7.74%, and the waiting time optimization effect of passengers is 6.583%.

scholarly journals Hybrid Mobile-App. on Multi-MEC Platforms in NFV Environment

2018 ◽  
Vol 7 (4.38) ◽  
pp. 383
Author(s):  
Jinsul Kim ◽  
Akm Ashiquzzaman ◽  
Van Quan Nguyen ◽  
Sang Woo Kim
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Large Scale ◽  
Web Applications ◽  
Rapid Development ◽  
Query Languages ◽  
Vital Role ◽  
Mobile App ◽  
Network Function Virtualization ◽  
Nosql Databases

In recent times, practicality of web applications has become more reliant upon big-data orientated materials such 4K videos, hi-def. resolution images, lossless audios and massive texts. Structured Query Languages (SQL) faces compatibility issues with large scale databases. Because of this data storage problem, NoSQL databases are used for storing big-data. NoSQL databases have been recently gaining traction with many options such MongoDB, CouchDB, Redis and Apache Cassandra. One of the major restrictions companies, enterprises and developers encounter during developing an application is multiplicative cost of building a native programing across different platforms. Besides, network Function Virtualization (NFV) plays a vital role for providing services for utilizing such applications in larger and more effective scale. Hence, in this paper, we discussed our main motivation behind selecting Iconic Framework, a hybrid system for rapid development real-time application based on Firebase in the NFV environment cooperating with Mobile Edge Computing (MEC). As a result, this approach provides comparatively flexible features.  

Improving the performance of precision poverty alleviation based on big data mining and machine learning

2020 ◽  
pp. 1-12
Author(s):  
Lejie Wang
Keyword(s):  
Machine Learning ◽  
Economic Growth ◽  
Big Data ◽  
Low Income ◽  
Large Scale ◽  
Rapid Development ◽  
Machine Learning Algorithms ◽  
Internet Technology ◽  
Rapid Economic Growth ◽  
Causes Of Poverty

Since the reform began in our country, with the rapid economic growth in recent years, the income level has grown extremely unequal, and it is difficult for the low-income poor to benefit from the rapid economic growth. The most important prerequisite for the fight against poverty is the accurate identification of the causes of poverty. To date, our country has not reached the level of maturity required to accurately study the causes of poverty in various households. However, with the rapid development of Internet technology and big data technology in recent years, the application of large-scale data technology and data extraction algorithms to poverty reduction can identify truly poor households faster and more accurately. Compared with traditional machine learning algorithms, there are no machine storage and technical constraints, can use a large amount of data and rely on multiple data samples.

scholarly journals Achieving Message-Encapsulated Leveled FHE for IoT Privacy Protection

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Weiping Ouyang ◽  
Chunguang Ma ◽  
Guoyin Zhang ◽  
Keming Diao
Keyword(s):  
Internet Of Things ◽  
Privacy Protection ◽  
Large Scale ◽  
Homomorphic Encryption ◽  
Rapid Development ◽  
Information Leakage ◽  
The Internet ◽  
Public Key Encryption ◽  
Fully Homomorphic Encryption ◽  
The Internet Of Things

The rapid development of the Internet of Things has made the issue of privacy protection even more concerning. Privacy protection has affected the large-scale application of the Internet of Things. Fully Homomorphic Encryption (FHE) is a newly emerging public key encryption scheme, which can be used to prevent information leakage. It allows performing arbitrary algebraic operations on data which are encrypted, such that the operation performed on the ciphertext is directly transformed into the corresponding plaintext. Recently, overwhelming majority of FHE schemes are confined to single-bit encryption, whereas how to achieve a multibit FHE scheme is still an open problem. This problem is partially (rather than fully) solved by Hiromasa-Abe-Okamoto (PKC′15), who proposed a packed message FHE scheme which only supports decryption in a bit-by-bit manner. Followed by that, Li-Ma-Morais-Du (Inscrypt′16) proposed a multibit FHE scheme which can decrypt the ciphertext at one time, but their scheme is based on dual LWE assumption. Armed with the abovementioned two schemes, in this paper, we propose an efficient packed message FHE that supports the decryption in two ways: single-bit decryption and one-time decryption.

A Large-Scale Device Collaboration Resource Selection Method with Multi-QoS Constraint Supported

2010 ◽  
Vol 143-144 ◽  
pp. 894-898
Author(s):  
Xiao Hui Rong ◽  
Pan Deng ◽  
Feng Chen
Keyword(s):  
Resource Selection ◽  
Large Scale ◽  
Rapid Development ◽  
Prototype System ◽  
Model Composite ◽  
Performance Requirements ◽  
System A ◽  
Relation Model ◽  
The Internet Of Things

With the rapid development of the “Internet of Things” and large-scale area management, device collaboration has developed to the stage of large-scale device collaboration. Aiming at the large-scale, dynamics and real-time of the large-scale device collaboration system, in order to ensure the performance of the large-scale device collaboration system, a Quality of Service(QoS) model of large-scale device collaboration is proposed, which contains device QoS model, composite QoS model and QoS relation model. Based on the model, an algorithm of the resource selection in large-scale device collaboration system is presented. Finally, the result of simulation experiments on the large-scale device collaboration prototype system shows that the method can satisfy the performance requirements of the large-scale device collaboration system.

scholarly journals Assessment of the Awareness of Nigerian Professionals in the Built Environment on the Big Data analytics (BDA) Applications in the Construction Industry.

2021 ◽  
pp. 1-7
Author(s):  
Emmanuel Jesse Amadosi
Keyword(s):  
Big Data ◽  
Built Environment ◽  
Data Analytics ◽  
Large Scale ◽  
Rapid Development ◽  
Big Data Analytics ◽  
Strong Relationship ◽  
Large Scale Data ◽  
Scale Data ◽  
Structured Questionnaire

With rapid development in technology, the built industry’s capacity to generate large-scale data is not in doubt. This trend of data upsurge labelled “Big Data” is currently being used to seek intelligent solutions in many industries including construction. As a result of this, the appeal to embrace Big Data Analytics has also gained wide advocacy globally. However, the general knowledge of Nigeria’s built environment professionals on Big Data Analytics is still limited and this gap continues to account for the slow pace of adoption of digital technologies like Big Data Analytics and the value it projects. This study set out to assess the level of awareness and knowledge of professionals within the Nigerian built environment with a view to promoting the adoption of Big Data Analytics for improved productivity. To achieve this aim, a structured questionnaire survey was carried out among a total of 283 professionals drawn from 9 disciplines within the built environment in the Federal Capital Territory, Abuja. The findings revealed that: a) a low knowledge level of Big Data exists among professionals, b) knowledge among professional and the level of Big Data Analytics application have strong relationship c) professional are interested in knowing more about the Big Data concept and how Big Data Analytics can be leveraged upon. The study, therefore recommends an urgent paradigm shift towards digitisation to fully embrace and adopt Big Data Analytics and enjoin stakeholders to promote collaborative schemes among practice-based professionals and the academia in seeking intelligent and smart solutions to construction-related problems.

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