Enterprise Users Research Articles

Research on the Construction Effect Evaluation System of Smart CBD: A Case Study in Jiangbei New District, Nanjing, China

With the update and development of technologies such as IoT, the smart city revolution is boosting construction. Several excellent CBD construction projects designed in smart city construction models have emerged around the world. Exploring the construction model of the CBD through the establishment of an evaluation index system of construction effect is of great significance for CBD’s future development. The main objective of this study is to analyze the factors that affect the effectiveness of the construction of the CBD by using the Delphi method and AHP and to provide a systematic method to establish an index evaluation system for the construction effect of the smart CBD, which reflects the influencing factors of the construction effect in the CBD district from six perspectives: construction, management, service, innovation, strategy, and users. This paper also introduces the construction and development of CBD in the Jiangbei New District in Nanjing, China, and uses the indicator system established in this study to evaluate its construction effect. This paper establishes a four-level indicator system, from general to specific, and discusses the degree of impact of each indicator on the effectiveness of smart CBD construction. In order to obtain the initial evaluation data more accurately, this study classified the survey subjects into three categories, which are managers, commercial enterprise users, and ordinary residents. Finally, through the analysis of the evaluation results, the effectiveness of the construction of the Jiangbei New District CBD was evaluated, and the focus of the future smart CBD construction was pointed out. The evaluation index system for the construction effect of the smart CBD established is considered to supply a good foundation for the CBD-related study.

Analysis of Enterprise Shared Resource Invocation Scheme based on Hadoop and R

The response rate and performance indicators of enterprise resource calls have become an important part of measuring the difference in enterprise user experience. An efficient corporate shared resource calling system can significantly improve the office efficiency of corporate users and significantly improve the fluency of corporate users' resource calling. Hadoop has powerful data integration and analysis capabilities in resource extraction, while R has excellent statistical capabilities and resource personalized decomposition and display capabilities in data calling. This article will propose an integration plan for enterprise shared resource invocation based on Hadoop and R to further improve the efficiency of enterprise users' shared resource utilization, improve the efficiency of system operation, and bring enterprise users a higher level of user experience. First, we use Hadoop to extract the corporate shared resources required by corporate users from the nearby resource storage computer room and terminal equipment to increase the call rate, and use the R function attribute to convert the user’s search results into linear correlations, according to the correlation The strong and weak principles are displayed in order to improve the corresponding speed and experience. This article proposes feasible solutions to the shortcomings in the current enterprise shared resource invocation. We can use public data sets to perform personalized regression analysis on user needs, and optimize and integrate most relevant information.

Identity Authentication with Association Behavior Sequence in Machine-to-Machine Mobile Terminals

With the rapid development of machine-to-machine (M2M) mobile smart terminals, M2M services can be used in a wide range of industries, including such as tele-medicine, remote meter reading and public security. Since different industries and enterprise users have different requirements for M2M specific applications, the security identity authentication of M2M mobile terminals is particularly worthy of attention. Existing methods can effectively solve the unsustainable problem of one-time verification, however they cannot address the dynamic relevance characteristics of user behavior sufficiently. Thus, the accuracy of user identity authentication needs to be further improved. In this paper, we propose a terminal identity authentication technology based on user association behavior analysis. In order to identify the abnormal login during each behavior process of authenticated user, we take largest coincident part of the user behavior sequence and short coincide into consideration. In addition, we propose Behavior Common Subsequence Sequence Similarity Algorithm based on the traditional sequence pattern of Behavior Common Subsequence(BCS). The experimental results demonstrate that the proposed method can effectively improve the accuracy of user’s behavioral sequence, and prove the uniqueness of different sequences on the other hand.

BitMine: An End-to-End Tool for Detecting Rowhammer Vulnerability

Rowhammer is a destructive software-induced DRAM fault, which an attacker can leverage to break system security. Both individual customers and enterprise users (e.g., cloud providers) might refrain from using a computing system if it is vulnerable to rowhammer vulnerability. In this paper, we provide the first end-to-end tool, coined BitMine, that systematically assesses a DRAM chip’s vulnerability to rowhammer bit flips. BitMine is an extension of DRAMDig. As DRAM address mappings are proprietary techniques and critical in inducing rowhammer bit flips, DRAMDig, our prior work, leverages domain knowledge to efficiently and deterministically reverse-engineer DRAM address mappings on Intel machines. By incorporating DRAMDig, BitMine configures three key parameters, i.e., hammer methods , hammer patterns , data patterns , on the effectiveness of finding rowhammer bit flips. BitMine by default implements 13 hammer methods, 4 hammer patterns and 16 data patterns and is extensible to support more. We evaluate DRAMDig and BitMine against multiple machine models that combine different DRAM chips and Intel microarchitectures. Our experiment results show that DRAMDig efficiently uncovers a deterministic DRAM address mapping for each machine model, and every implemented parameter in BitMine has its distinct effectiveness in triggering bit flips for different machine models.

Optical service unit (OSU)-based next generation optical transport network (NG OTN) technology and verification

With the development of transmission technologies, optical transport network (OTN) has evolved from a digital encapsulation technology to a network technology that supports multi-service transport. As the OTN gradually expands to the edge of the metropolitan area network (MAN) and enterprise users have higher quality requirements on bandwidth and service security, more and more private line services are provided via OTN network. Therefore, the OTN container is transformed from an optical data unit (ODU) to an optical service unit (OSU). The OTN industry has studied several OSU technologies to fit the development trends of NG OTN, aiming at transporting 2 Mbit/s to 100 Gbit/s constant bit rate (CBR) services and Ethernet services with high efficiency and more flexibilities, as well as reducing OTN network deployment and operation and maintenance (O&M) costs. In this work, several key technologies in NG OTN are introduced including NG OTN technical architecture, OSU frame format, client mapping and OSU multiplexing. Based on these technologies, we (China Mobile) took the lead in conducting the industry's first NG OTN test in Qingdao, which verified the service transport capability and lower latency performance of OSUs and promoted the maturity of the NG OTN industry chain.

Sharing Enterprise Cloud Securely at IBM

Cloud platforms have been relieving users from managing their own infrastructures and simplifying the task of application development. In recent years, containerized cloud platforms further accelerate the development process and provide a fine-grained billing model. For enterprise users, many challenges exist when migrating their applications to cloud platforms. The challenges include resource isolation, resource sharing, and usability. This article presents our approaches to securely and effectively share the already-shared cloud resources at IBM Chief Information Office. Several key approaches are discussed, such as cluster isolation, cluster automation, and identity and access management. We also collect various types of metrics as we are enabling the shared cloud. Our work shows great promise for migrating enterprise users and their data to the cloud.

A System Dynamics Model for the Diffusion of Cloud Manufacturing Mode With Evolutionary Game Theory

Cloud manufacturing (CMfg) is emerging as an advanced green and efficient manufacturing mode for the manufacturing industry. It has gained extensive attentions owing to the unique manufacturing mode and advantages. However, current research pays little attention on the diffusion and adoption of CMfg mode. Based on the diffusion of innovation (DOI) theory, in this study a three-stage diffusion model of CMfg mode is developed from the perspective of manufacturing enterprises. Furthermore, the system dynamic (SD) approach is employed to study the CMfg mode adoption behaviors of manufacturing enterprises. The causal loop diagrams including user market submodule, enterprise revenue submodule and diffusion module are analyzed firstly, and a SD diffusion model with evolutionary game theory is established. At last, CMfg diffusion process is simulated by considering the commission rate, government’s subsides, user membership fee of cloud platform and user acceptance intention The results show that subsidy for enterprise users is better than that for CMfg service providers in promoting the diffusion of CMfg mode. User acceptance intention and membership fees are another two key factors.

Fine grained access control using H-KCABE in cloud storage

Cloud computing is used to store data in cloud storage through remotely. Cloud services can reach from small scale to large scale applications in low cost budget. Through these services users are able to access and retrieve data very constrainedly in low cost budget. It is used for producing data cloud service provider. It’s taking care of data to keep confidentially from entrusted access. It reduces most of the cloud security problems. This is achieved by some firewalls, cryptographic algorithms, antivirus software. The above techniques could not product data effectively which is shared by enterprise users who confidentially maintain the data during transactions among worldwide. Through simple encryption algorithm we cannot provide fine grained access control to the data in cloud storage. But its different from perfomance, scalability, reliability and other security parameters of cloud computing. In this paper we propose H-KCABE encryption algorithm in HABE model with some small modification and make better performance by re-encryption process. Through this we can easily achieve fine grained access control of cloud data than existing technique.

A Secure Cloud Data Sharing Protocol for Enterprise Supporting Hierarchical Keyword Search

Cloud storage becomes the priority for storing and sharing data for enterprise users. Encrypting prior to uploading data to the cloud is the best way to protect business secrets, however, it hinders the convenient operations on plaintexts, such as searching over the cloud data. In addition, employees in an enterprise have multiple layer structures and a higher layer employee should have the privilege to monitor the lower layer employees’ data to check if these users violate the regulation without letting the employees be aware of. Public key encryption with keyword search (PEKS) is a well-known cryptographic primitive suitable for secure cloud storage, which supports keyword search without decryption in public key encryption settings. Unfortunately, no existing PEKS scheme supports the monitoring function without authorization from the sender. To address this issue, we propose a variant of PEKS named Hierarchical Public Key Encryption with Keyword Search (HPEKS) and provide a semi-generic construction utilizing a public key tree (PKTree) and a PEKS scheme. To better suit for the enterprise secret data sharing, we build an advanced HPEKS scheme, named designated-tester decryptable hierarchical public key encryption with keyword search (dDHPEKS), which enjoys stronger security and integrates the public key and symmetric key encryptions. We prove our dDHPEKS scheme secure under the security definition in the random oracle model. Particularly, it satisfies the security against outside offline keyword guessing attacks and furthermore, enjoys the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">transparency</i> property so that the sender does not need to know the internal hierarchy structure of an enterprise in order to share encrypted data to the enterprise. Theoretical evaluation and concrete experiments show that our dDHPEKS scheme has comparable running efficiency with existing PEKS schemes.

Based on Numerical Control Fold the Speed of Curved Robot System from Meet Speed with Swing to Study

In order to solve the current domestic sheet metal enterprise users has increased rapidly, but there are still most bending machine or manual operation problem, involving the employment difficult problems, according to labor shortage problem, must promote intelligent enterprise transformation, realize the exchange Labour “machine”, the development and application of nc bending robot system is the inevitable developing trend of sheet metal industry, speed adaptive speed and oscillation is important in the development and application of nc bending robot system. This paper describes the system model of the speed adaptive speed and swing of the CNC bending robot system, and analyzes the performance of the speed adaptive speed and swing system comprehensively. The results of computer simulation show the consistency between simulation and theory.

Conversational BI

Business intelligence (BI) applications play an important role in the enterprise to make critical business decisions. Conversational interfaces enable non-technical enterprise users to explore their data, democratizing access to data significantly. In this paper, we describe an ontology-based framework for creating a conversation system for BI applications termed as Conversational BI. We create an ontology from a business model underlying the BI application, and use this ontology to automatically generate various artifacts of the conversation system. These include the intents, entities, as well as the training samples for each intent. Our approach builds upon our earlier work, and exploits common BI access patterns to generate intents, their training examples and adapt the dialog structure to support typical BI operations. We have implemented our techniques in Health Insights (HI) , an IBM Watson Healthcare offering, providing analysis over insurance data on claims. Our user study demonstrates that our system is quite intuitive for gaining business insights from data. We also show that our approach not only captures the analysis available in the fixed application dashboards, but also enables new queries and explorations.

Research on Cluster Monitoring and Prediction Platform based on Zabbix Technology

With the rapid development of Internet and computer technology, cluster technology has been widely applied to the explosive growth of network data. With the successful construction of the cluster and the continuous expansion of nodes, the reliability and stability of the cluster itself has become an important factor that cannot be ignored, so various monitoring systems emerge. Based on the above reasons, this paper proposes a real-time monitoring and prediction platform for enterprise users based on Zabbix. The monitoring and prediction platform monitors all kinds of indexes in the cluster in real time and predicts important performance indexes in real time. When monitoring or predicting the abnormal situation, the system can warn the users, which is conducive to the real-time understanding of the cluster operation. The short-term real-time prediction model of cluster monitoring index is introduced in detail in this paper. Finally, the real-time monitoring function of indicators is designed and analyzed based on the prediction model.

An efficient and secure data storage in cloud computing using modified RSA public key cryptosystem

&lt;span lang="EN-US"&gt;Cloud Computing is the ability to improve the utility or train new human resources without investing in new infrastructure, or add capabilities to existence without the latest software licensing. It expanded the capabilities of Information Technology (IT). From the past few years, cloud computing has developed from a good business concept in the best rising sectors of the IT industry. But more information on individuals and companies was put in the cloud, and concerns began to think about how secure the cloud environment was. Despite cloud surrounding structures, enterprise users still do not want to expand their business in the cloud. Security reduces the growth of cloud computing and continues to spread the market with complexity with data privacy and data protection. The security of cloud computing has constantly been an significant aspect of improved quality of service from cloud service providers. Data storage in the cloud has a problem related to data security. However, cloud computing construct many new security challenges which have not been well examine. In order to ensure that the user's data in the cloud is secure, we have proposed an effective mechanism with a distinctive feature of data integrity and privacy. This paper focusing on problems relating to the cloud data storage techniques and security in virtual environment. We recommend a method for providing data storage and security in cloud using public key Cryptosystem, which uses the concept of the modified RSA algorithm to provide better security for the data stored in the cloud. &lt;/span&gt;

Exploiting WiMAX for covert transmission of secret data over fading channel

SummaryWorldwide Interoperability for Microwave Access (WiMAX) is the broadband wireless access technique that provides rapid broadband services to domestic and enterprise users. Owing to the broadcast characteristic of the WiMAX system, transmitted data security is crucial, particularly when the messages are confidential. In this work, exploiting of WiMAX to convey sensitive secret information is presented. In the first phase, we exploited conventional WiMAX (fast Fourier transform [FFT]‐WiMAX), and in the second phase, we propose a wavelet packet transform (WPT)–based WiMAX for covert communication. The quality evaluation of both covert transmission models (FFT‐WiMAX and WPT‐WiMAX) over the fading channel is done in this work. The experimental outcomes of the proposed WPT‐WiMAX covert transmission system reveal a significant enhancement in error rate (bit error rate) and peak signal‐to‐noise ratio for a given signal‐to‐noise ratio.

New group user based privacy preserving cloud auditing protocol

Cloud storage is now a very popular service for many enterprises and their users. However, ensuring the integrity of the outsourced data is a very challenging problem. The concept of provable data possession is very effective for solving this problem. Based on this concept, in this paper we propose a new group-user-based privacy-preserving secure cloud auditing protocol for enterprise users and analyse its security. This protocol is also constructed on a recently proposed lightweight and privacy-preserving secure cloud auditing scheme for group users via a third-party medium. We point out that scheme is not secure and that the authenticators corresponding to the outsourced data blocks can be easily forged, that means the cloud servers can delete all the outsourced data but can still give correct proof for the possession of outsourced data, while our protocol has no such shortcoming. The performance analysis results demonstrate that our proposal is very practical and can have large-scale applications.

A Blockchain-Driven Supply Chain Finance Application for Auto Retail Industry.

In this paper, a Blockchain-driven platform for supply chain finance, BCautoSCF (Zhi-lian-che-rong in Chinese), is introduced. It is successfully established as a reliable and efficient financing platform for the auto retail industry. Due to the Blockchain built-in trust mechanism, participants in the supply chain (SC) networks work extensively and transparently to run a reliable, convenient, and traceable business. Likewise, the traditional supply chain finance (SCF), partial automation of SCF workflows with fewer human errors and disruptions was achieved through smart contract in BCautoSCF. Such open and secure features suggest the feasibility of BCautoSCF in SCF. As the first Blockchain-driven SCF application for the auto retail industry in China, our contribution lies in studying these pain points existing in traditional SCF and proposing a novel Blockchain-driven design to reshape the business logic of SCF to develop an efficient and reliable financing platform for small and medium enterprises (SMEs) in the auto retail industry to decrease the cost of financing and speed up the cash flows. Currently, there are over 600 active enterprise users that adopt BCautoSCF to run their financing business. Up to October 2019, the BCautoSCF provides services to 449 online/offline auto retailors, three B2B asset exchange platforms, nine fund providers, and 78 logistic services across 21 provinces in China. There are 3296 financing transactions successfully completed in BCautoSCF, and the amount of financing is ¥566,784,802.18. In the future, we will work towards supporting a full automation of SCF workflow by smart contracts, so that the efficiency of transaction will be further improved.

Scalable big data modelling

In the information age, data integration has become easier than ever. Enterprises integrate a wide range of data sources to enrich big data lakes. Enterprise big data lake made data consumption simpler and faster for all stakeholders. Often, stakeholders face challenges to limit data that they need for analysis and making effective decisions. As more data from ever-growing data sources is coming in, users are flooded with a variety of data. Data models alleviated the pain to serve insights to enterprise users. Data models provided insights after data cleansing, aggregating, and applying business rules. As data models in big data grow, queries and analysis require processing the large volume of data and big joins. It leads to long response and processing times. Data modelling in big data platforms needs attention to effectively cleanse, organise, and store big data to ensure timely availability of enterprise insights. As the scale is a critical aspect of the big data platform, big data should be modelled in a way that accessibility and delivery of insights should not be affected when the scale goes up. This paper presents best practices to model structured and semi-structured data in the big data platform.

Scalable big data modelling

In the information age, data integration has become easier than ever. Enterprises integrate a wide range of data sources to enrich big data lakes. Enterprise big data lake made data consumption simpler and faster for all stakeholders. Often, stakeholders face challenges to limit data that they need for analysis and making effective decisions. As more data from ever-growing data sources is coming in, users are flooded with a variety of data. Data models alleviated the pain to serve insights to enterprise users. Data models provided insights after data cleansing, aggregating, and applying business rules. As data models in big data grow, queries and analysis require processing the large volume of data and big joins. It leads to long response and processing times. Data modelling in big data platforms needs attention to effectively cleanse, organise, and store big data to ensure timely availability of enterprise insights. As the scale is a critical aspect of the big data platform, big data should be modelled in a way that accessibility and delivery of insights should not be affected when the scale goes up. This paper presents best practices to model structured and semi-structured data in the big data platform.

Mobile fog based secure cloud-IoT framework for enterprise multimedia security

With the dawn of computing era in the enterprise business, the methodologies for business have changed significantly. The enterprise information in the form of multimedia data is now being created and extensively shared over the internet. On the other side, the rapid and continuous growth of enterprises is causing the computing systems to evolve rapidly and continuously to match the pace of growth. This has led to the evolution of new technologies related to the enterprise environment. The advent of Internet of Things (IoT) and Cloud Computing in the enterprise environment is the result of its rapid and continuous requirement. But, their use has increased the complexity of enterprise computing systems especially in the domain of Enterprises Multimedia Security (EMS). In Cloud-IoT environment, the Enterprise Multimedia Data (EMD) stored at remote Cloud premises and subject to various kinds of vulnerabilities like data leakage, data modification and confidentiality breach. Hence, this paper proposes a distinctive EMS approach using Fog and Cloud to store different types of data and maintains their co-relation to prevent various EMD security breaches. In this paper, a Mobile Fog based Cloud-IoT framework for EMS has been designed in which only encrypted data is stored at the Cloud and its keys are securely stored at the enterprise premises on the Fog layer. It uses Advanced Encryption Standard (AES) for encryption, Message Authentication Code (MAC) for data integrity, Secure Socket Layer (SSL) for safe data transmission, tokenizer, hashing and data pointers for easy data search on Fog and Cloud. It stores the encryption/decryption and MAC keys encrypted with Master Encryption Key (MEK) securely at the enterprise premises on the Fog layer. For assured security of keys, the framework doesn’t store MEK at any place on the network. The experimental result analyses acknowledge the highest level of security, better scalability and storage management as well as searching time efficiency in the proposed framework. The overall results analysis show that the proposed framework is highly secure to protect the multimedia data of an enterprise from the inside and outside threats and access of data is provided to the authorized enterprise users.

Efficient Data Placement and Replication for QoS-Aware Approximate Query Evaluation of Big Data Analytics

Enterprise users at different geographic locations generate large-volume data that is stored at different geographic datacenters. These users may also perform big data analytics on the stored data to identify valuable information in order to make strategic decisions. However, it is well known that performing big data analytics on data in geographical-located datacenters usually is time-consuming and costly. In some delay-sensitive applications, the query result may become useless if answering a query takes too long time. Instead, sometimes users may only be interested in timely approximate rather than exact query results. When such approximate query evaluation is the case, applications must sacrifice timeliness to get more accurate evaluation results or tolerate evaluation result with a guaranteed error bound obtained from analyzing the samples of the data to meet their stringent timeline. In this paper, we study quality-of-service (QoS)-aware data replication and placement for approximate query evaluation of big data analytics in a distributed cloud, where the original (source) data of a query is distributed at different geo-distributed datacenters. We focus on the problems of placing data samples of the source data at some strategic datacenters to meet stringent query delay requirements of users, by exploring a non-trivial trade-off between the cost of query evaluation and the error bound of the evaluation result. We first propose an approximation algorithm with a provable approximation ratio for a single approximate query. We then develop an efficient heuristic algorithm for evaluating a set of approximate queries with the aim to minimize the evaluation cost while meeting the delay requirements of these queries. We finally demonstrate the effectiveness and efficiency of the proposed algorithms through both experimental simulations and implementations in a real test-bed, real datasets are employed. Experimental results show that the proposed algorithms are promising.