Data Service Providers Research Articles

Research on Data Management of Power Grid Enterprises Based on Data Middle Platform

At present, the construction process of power grid enterprise information system mainly adopts the “vertical integration” system structure, with high construction cost, long period, non-sharing of data and low quality. By introducing the concept of data middle platform.to reaggregate and reconstruct unified supervision of enterprise data, it provides accurate and reliable, shareable and reusable data application services with business logic for front-end business, and realizes intelligent interconnection and data sharing in all aspects of power system. This paper puts forward the idea of data center, and puts forward data management methods from master data, data warehouse and metadata management respectively, so as to provide data service support for the development of power grid business towards intelligent and efficient direction.

IoT root union: A decentralized name resolving system for IoT based on blockchain

A name resolving system services as the entrance to the IoT data, thus protecting the name resolving process and making it straight and simple are critical for the widespread application of IoT services. However, due to the centralized name management structure of the existing IoT name resolving systems, it is hard to secure the name resolving process from being tampered by the root owner of the name resolving system; due to the diversity and heterogeneous of the existing name resolving systems, it is hard to provide an unified name resolving service because the existing name resolving systems can hardly be compatible with each other. Herein, it is necessary to construct a general entrance to the existing IoT name resolving systems, and ensure that no IoT name could be malicious tampered.In this paper, we propose the IoT-RU, a decentralized name resolving system for IoT. To persuade the IoT data service providers to participate in our IoT-RU, we construct its name resolving database on the blockchain ledger. To enable the adoption of the existing IoT naming schemes, IoT-RU integrates various name resolving schemes to resolve the IoT service providers in our system, and leave the job of locating individual IoT devices to the IoT service providers. We further investigate the feasibility of name management and resolving based on the current blockchain architectures. Based on the Ethereum platform, we construct a prototype of our name resolving system, and the corresponding evaluation indicates that our name resolving system manages to provide acceptable and scalable name resolution services.

Data reduction algorithm for correlated data in the smart grid

Smart grids are intelligent electrical networks that incorporate information and communication technology (ICT) to provide data services for the power grid. In this paper, the ICT requirements for monitoring and control of the neighbourhood area network level of the smart grid, with particular emphasis on making the ICT infrastructure energy efficient, are analysed. One approach to provide energy efficiency in the communication system is to develop a data reduction algorithm to reduce the volume of data prior to transmission. Thus, a data compression technique called DRACO (data reduction algorithm for correlated data) that shows a reasonable compression ratio while using network resources efficiently is designed and developed. DRACO can be applied to data with a high data sampling rate, and can transmit the essential information with compression ratios of 70%–99%. The results of applying DRACO on real data collected by devices located in the University of Manchester campus are discussed, followed by the evaluation and validation of DRACO by comparing it with other available techniques. Finally, it is concluded that DRACO is suitable for smart grid applications since it optimizes the network resource consumption and reduces the communication energy cost while maintaining the integrity and quality of data.

Improving Privacy in Data Service Composition

Today, the de-facto standard mechanism for data service providers to share their services is through web-service interfaces; clients invoke the service through request messages and receive the data as payloads in the corresponding response messages. Typically, clients need information beyond what any single provider offers; in such cases, multiple data services must be composed to provide a complete solution meeting the client needs. The term “data-service composition” refers to a unified interface that delivers to the client the data it needs in response to a single request message, as if it were available withing a single source. Data-service composition is useful and convenient for the requesting client, but raises privacy concerns since a participating data-service provider potentially can infer information about the data held by other providers. In this paper, we propose a data-service composition method that relies on a mediator for the communication between any two service providers, ensures that the mediator is strictly following the data-composition plan, and maintains privacy between the mediator and service providers. The data service provider first authenticates that the input data is coming from the correct source as per the composition plan, and this is done whilst ensuring complete privacy between the mediator and other service providers. Similarly, data service providers also authenticate the destination of their output data. The approach is validated and its performance evaluated using a real world online retail dataset.

Design and development of a novel symmetric algorithm for enhancing information security

Because of the tremendous rise in internet-based cybercrime, the safety of data is becoming increasingly important in order for the internet to continue providing its many features and benefits. The largest problem for data owners and service providers is ensuring the security and privacy of their data. Digital technology has become an integral element of our daily lives. Technology plays a vital part in everything from online shopping to online banking to government infrastructure. Cyber-attacks, on the other hand, are a blemish on the digital landscape. As a result, the authors devised a new symmetric algorithm that utilizes a private key and delivers a more scalable, secure, and speedy algorithm solution. As a result of this algorithm's efforts, the security hazards to confidential material will be greatly reduced. This study focuses mostly on the issues that data security faces when working with the most current technology.

Deep Reinforcement Learning Based Dynamic Spectrum Competition in Green Cognitive Virtualized Networks

This paper examines the optimal spectrum competing strategy for a virtual network operator in cognitive cellular networks with energy-harvesting base stations. In the scenario for this study, multiple cognitive virtual network operators (CVNOs) obtain spectrum resources from a mobile network operator via spectrum sensing and leasing in order to provide data services to their subscribers. Compared to traditional spectrum leasing via long-term contract, spectrum acquired by sensing is usually cheaper but is unreliable due to the stochastic activities of the licensed users. The CVNOs need to determine the optimal sensing and leasing amount to satisfy the needs of subscribers while guaranteeing a low leasing cost. We aim to find an efficient spectrum sensing and leasing scheme for a CVNO in order to maximize its utility in the long run. The problem is first formulated as the framework of a sequential decision process considering the dynamics of users’ activities, spectrum prices, and harvested energy. We then develop a deep reinforcement learning algorithm that uses deep neural networks as function approximators so the CVNO can learn the optimal decision policy by interacting with the environment. We analyze the performance of our proposed scheme through extensive simulations. The experiment results show that the proposed mechanism can significantly improve the CVNO’s long-term benefit compared to other learning and non-learning methods.

Improving Data Services of Mobile Cloud Storage with Support for Large Data Objects using OpenStack Swift

Providing data services support for large file upload and download is increasingly vital for mobile cloud storage. There is an increase in mobile users whose data access trends show more access and large file sharing. It is a challenging task for Mobile Application Developers to handle upload and retrieve large files to/from a mobile app because of difficulties with latency, bandwidth, speed, errors, and disruptions to service in a wireless mobile environment. Some scenarios require these large files to be used offline, sometimes to be updated by a single user, and sometimes be shared among all other users. The Wireless mobile environment must consider mobile user’s constraints, such as frequent disconnections and low bandwidth, which affect the ability to handle data and transactions management. The primary objective of this study is to propose a cloud-based Mobile Sync service (sometimes referred as Mobile backend as a Service) with OpenStack Swift object storage to manage large objects efficiently using two main techniques of segmentation and object chunking with compression in a mobile cloud environment. This work further contributed to a prototype implementation of the proposed framework and provides Application Programming Interface (API) consisting of Create, Read, and Delete queries and chunking operations and a lightweight sync protocol that can manage large file synchronization and access. The experimental findings with object-chunking tested size settings show that the proposed Mobile Sync framework can accommodate large files ranging from 100MB to 1GB and provides a decrease in upload/download synchronization times of 63.203% / 92.987%percent as compared to other frameworks.

A new resource allocation method in fog computing via non-cooperative game theory

IoT-based network systems use a modern architecture called fog computing, In which data providing data services is economical with low latency. This paper tends to solve the challenge of resource allocation in fog computing. Solving the resource allocation challenge leads to increased profits, economic savings, and optimal computing systems use. Here resource allocation is improved by making use of the combined algorithm Nash equilibrium and auction. In the proposed method, each player is assigned a matrix. Each player matrix includes fog nodes (FNs), data service subscribers (DSSs), and data service operators (DSOs). Each player generates the best strategy based on the other players strategy in all stages of the algorithm. The simulation results show that FNs profit in the combined Nash and Auction equilibrium algorithms is superior to the Stackelberg game algorithm.

Research on Agricultural Products Logistics and Supply Chain System Based on Computer Big Data Model

With the development of the market, the traditional production and circulation mode of agricultural products can no longer meet the requirements of market informatization and supply chain development. Therefore, it is necessary to study a new production and circulation method of agricultural products to solve the problems caused by traditional methods. We build an innovation system for agricultural product supply chains led by big data service providers, integrating agricultural product supply chain resources, reducing agricultural product circulation links, reducing costs, achieving agricultural product traceability, and ensuring agricultural product quality and safety. Based on this, this article proposes an agricultural product supply chain model based on an integrated management platform, and conducts in-depth research on the logistics operation logic of agricultural product supply chain to provide theoretical basis and data support for the design and development of the management platform.

Robust Time Series Factor Models

Abstract We introduce a robust regression estimator for time series factor models called the mOpt estimator. This estimator minimizes the maximum bias due to outlier generating distribution deviations from a standard normal errors distribution factor model, and at the same time has a high normal distribution efficiency. The efficacy of this estimator is demonstrated in applications to single factor and multifactor time series models. Extensive empirical investigation of the mOpt robust betas versus non-robust least square betas show that differences between the two estimates greater than 0.3 occur for about 18% of the stocks, and differences greater than 0.5 occur for about 7.5% of the stocks. We introduce and demonstrate the use of a robust statistical test for differences between mOpt and least squares factor model coefficients, and also a new robust model selection method that makes natural use of the mOpt regression estimator. It is highly recommend that practitioners and data service providers compute robust mOpt betas as a standard practice complement to least squares betas.

PERANCANGAN PIRANTI LUNAK RESPONSIVE UNTUK MONITORING RUANGAN SERVER MENGUNAKAN NODEMCU ESP8266 BERBASIS INTERNET OF THINGS

The role of the server is very important as a data service provider and control for an office that has a very large data usage and control system. This can also affect the temperature in the server room which has computer devices and other control systems because of its continuous use. The impact of temperature that is outside of its tolerance value due to the dead air conditioner without being noticed by the officer or server operator can result in damage to the hardware devices in the server room. Monitoring and measuring server room temperature is not possible to be done directly and accurately in all conditions due to several inhibiting factors in obtaining temperature information. With these problems, a temperature monitoring prototype was built using node MCU ESP 2866 and DHT 21 sensor with responsive web-based software so that it can be easily seen on various PC and smartphone monitors. This software can be used in server rooms to provide room temperature information to operators or server officers. The long-term objective of this research is expected to provide information Real-time temperature conditions in a responsive web-based server room.

A Big Data Demand Estimation Model for Urban Congested Networks

Abstract The origin-destination (OD) demand estimation problem is a classical problem in transport planning and management. Traditionally, this problem has been solved using traffic counts, speeds or travel times extracted from location-based sensor data. With the advent of new sensing technologies located on vehicles (GPS) and nomadic devices (mobile and smartphones), new opportunities have emerged to improve the estimation accuracy and reliability, and more importantly to better capture the dynamics of the daily mobility patterns. In this paper we frame this new data in a comprehensive framework which estimates origin-destination flows in two steps: the first step estimates the total generated demand for each traffic zone, while the second step adjusts the spatial and temporal distribution on the different OD pairs. We show how mobile data can be used to obtain OD matrices that reflect the aggregated movements of individuals in complex and large-scale instances, while speed information from floating car data can be used in the second step. We showcase the added value of big data on a realistic network comprising Luxembourg’s capital city and its surrounding. We simulate traffic by means of a commercial simulation software, PTV-Visum, and leverage real mobile phone data from the largest telco operator in the country and real speed data from a floating car data service provider. Results show how OD estimation improves both in solution reliability and in convergence speed.

Fundamental Limits of Data Utility: A Case Study for Data-Driven Identity Authentication

Big data can help with providing valuable perceptions into business activities and disclosing the potential benefits. Advances in machine learning and deep learning technologies make it easier to achieve significant performance in a wide range of domains from city planning and marketing analysis to credit evaluation and identity theft detection. However, it still requires great efforts in selecting efficient learning algorithms and precise model parameters that are deemed confidential in the light of experience. Also worth noting is that there is a fundamental gap between impracticable business requirements and the available value of data reflected. The data holder or data service provider may not have a clear understanding of data interference. The solution to these two problems depends on the capability of predicting the data utility in advance, which raises a fundamental question: to what degree is the data utility predictable? In this work, we present a primary analytical framework for information-theoretic bounds of data utility and utilize the current state-of-the-art and representative algorithms to obtain the achievable lower bounds on a real-world data set. The gap between theoretical upper bounds and achievable lower bounds indicates that the achievable lower bounds can still be optimized for performance.

Authenticity verification on social data outsourcing

In the social data transaction model, online social networks sell their collected social data to a third-party data service provider, which commonly resells such social data to data users for further mining potential information. However, the data service provider may not be trustworthy and collude with others to return fake data to users. To prevent such malicious activities, data users should verify the correctness and completeness of social data purchased from the data service provider to make sure that no data would tamper and no qualifying results would be omitted. Accordingly, we first propose an authenticity verification scheme, called FakeDetection, for one-dimensional data query. To make our scheme becoming efficient, we further devise an enhanced probabilistic scheme FakeDetection+, which takes partial vertices and neighbors with the identical profile value to generate auxiliary information. To evaluate the efficiency of our schemes, we utilize the real Twitter datasets with 1.6M twitters to perform the experiments. The Twitter with FakeDetection+, takes 69K edges (47M in FakeDetection) into account to detect fake activities with a probability of more than 99%. For the computation overhead, the FakeDetection+ scheme only consumes 27.9s (3.8% of that in FakeDetection) to generate auxiliary information for a social graph with 1.6M twitters and their social network.

Construction of campus global data system based on data middle office

Campus informatization has entered the data technology (DT) era. In order to solve the problems of inaccurate analysis results and inadequate decision support caused by insufficient available data dimensions in various business departments of the school, we put forward a method of using data middle office technology to gather all the data on the data platform, and let the whole campus share one data technology platform and one set of data assets. Taking the construction of global data warehouse system of Sun Yat sen University as an example, we build the data middle office architecture from bottom to top, design and construct the data hierarchical system of global data, and provide services for various data analysis applications at the upper level. Data middle office of Sun Yat-sen University has formed a set of efficient and reliable data asset system and data service capability (data capitalization and asset service) through its own platform capability and continuous nourishment of data by various businesses (business digitization). When schools need to build new business analysis applications, data middle office can quickly provide data services (data servitization) and respond quickly. One of our fundamental innovations is to separate "data assets" as a basic element, and integrate the data into the business value creation process as the means of production, and continuously generate value. Practice has proved that the construction of data hierarchical data system, in the provision of data services, the efficiency of data calculation and the accuracy of calculation results can well meet the business requirements of fast and accurate access to analysis results.

Privacy Is Dead - Solutions for Privacy-Enabled Collections and Use of Personal Health Information in Digital Era.

Today's digital information systems and applications collect every day a huge amount of personal health information (PHI) from sensor and surveillance systems, and every time we use personal computers or mobile phones. Collected data is processed in clouds, platforms and ecosystems by digital algorithms and machine learning. Pervasive technology, insufficient and ineffective privacy legislation, strong ICT industry and low political will to protect data subject's privacy have together made it almost impossible for a user to know what PHI is collected, how it is used and to whom it is disclosed. Service providers' and organizations' privacy policy documents are cumbersome and they do not guarantee that PHI is not misused. Instead, service users are expected to blindly trust in privacy promises made. In spite of that, majority of individuals are concerned of their privacy, and governments' assurance that they meet the responsibility to protect citizens in real life privacy is actually dead. Because PHI is probably the most sensitive data we have, and the authors claim it cannot be a commodity or public good, they have studied novel privacy approaches to find a way out from the current unsatisfactory situation. Based on findings got, the authors have developed a promising solution for privacy-enabled use of PHI. It is a combination of the concept of information fiduciary duty, Privacy as Trust approach, and privacy by smart contract. This approach shifts the onus of privacy protection onto data collectors and service providers. A specific information fiduciary duty law is needed to harmonize privacy requirements and force the acceptance of proposed solutions. Furthermore, the authors have studied strengths and weaknesses of existing or emerging solutions.

Anomaly traffic detection and correlation in Smart Home automation IoT systems

AbstractSmart building automation systems are increasingly the target of hacking attacks. Moreover, they may be used as a tool for attacks against targets located out of the native Home Area Network (HAN). These attacks are often resulted in changes in traffic volume, damaged packets, increased message traffic, and so on. Symptoms of attacks can be detected as anomalies in traffic model and recognized by a software agent run on Home Gateway. Although these anomalies are detected locally, it may help network provider to protect his resources as well as other resources of his clients. For that purpose, network operator should be able to recognize anomalies and correlate them on the network level. In this way, the network operator has the ability to protect both its own network and HANs of its clients. This article shows that Smart Home security might be coupled with the providers' network security policy. For that reason, security tasks should be performed both in HAN and providers' data center. This article describes a novel strategy for anomaly detection that provides shared responsibility between a service client and the network provider. It uses a machine learning approach for classifying the monitoring data and correlation in searching suspicious behavior across the network resources at the service provider's data center.

5G Media Data System Architecture

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">The existing 3rd Generation Partnership Project (3GPP) packet- switched streaming (PSS) architecture that was developed for 3G and 4G and evolved to carry streaming content with DASH [Dynamic and Adaptive Streaming over Hypertext Transfer Protocol (HTTP)], is now seen as too limited for 5G. 3GPP has, therefore, started developing a new media streaming architecture, considering the latest advances in the media industry and the features offered by the 5G system. Recognizing that, most media and video content delivered to the user is provided by several online service providers, the new 3GPP 5G media streaming architecture focuses on different collaboration and deployment models between mobile network operators and media data service providers. These collaboration and deployment models also cater to traditional broadcasters, which increasingly see the need for high-quality contributions via a mobile network, for example, to cover unplanned or transient events. The new architecture supports unicast downlink media data distribution and uplink streaming</i> .

Software Defined Networks using Mininet

A software defined network (SDN) is combined with centralized management by providing separation of the network, data and control planes. Different cloud computing environments, enterprise data centers and service providers are using this important feature. By implementing software defined data centers. Here in this paper we have used Mininet to demonstrate the applicability of SDN for different scalability. We study the performance of two SDN controllers – RYU and POX, that are implemented in Python using Mininet and D-ITG, Distributed Internet Traffic Generator. During this study we have used two network topologies, single and linear. The performance parameters used are maximum delay, average jitter, average bitrate. Experimental results demonstrated that the linear topology with RYU controller performs better as compared to single topology with POX controller for different network sizes.

An efficient privacy-preserving data query and dissemination scheme in vehicular cloud

Vehicular Cloud (VC) improves the utilization of idle resources in vehicles by providing data services for other vehicles or entities such as data computing and storage. However, the vehicles in the VC still face privacy and security issues, especially when querying data from the road side unit. To this end, in this paper, we propose an efficient privacy-preserving data query and dissemination scheme, named EPDQD, for VC. More precisely, by using the lightweight homomorphic encryption technique and the Chinese Remainder Theorem, the road side unit can aggregate and recover multiple data query requests without knowing the linkability of the data query request and its origin vehicle. EPDQD also adopts the message authentication code and non-interactive secure session keys to achieve the authentication of data query requests and the integrity of data transmission. Detailed security analysis indicates that EPDQD can resist various security threats. In addition, the performance evaluations demonstrate that EPDQD can significantly reduce computation complexity and communication overhead.