难度之选 - CCF 推荐期刊专刊信息9条

数据库管理与信息检索

Information and Management

Digital Business Transformation in Innovation and Entrepreneurship (DBTIE)

全文截稿: 2018-08-01

影响因子: 3.89

CCF分类: C类

中科院JCR分区:

  • 大类 : 工程技术 - 2区

  • 小类 : 计算机：信息系统 - 2区

网址: http://www.journals.elsevier.com/information-and-management/

Innovation and entrepreneurship are tightly coupled concepts. As stated by Drucker (1998), “Innovation is the specific tool of Entrepreneurs, the means by which they exploit change as opportunity of a different [new] business or a different [new] service… Entrepreneurs need to search purposefully for the sources of innovations, the changes and their symptoms that indicate opportunities for successful innovation”. The modern IT like social, mobile, analytics and cloud (SMAC) and emerging aspects like bigdata and internet-of-things is changing the way innovation and entrepreneurship are conceived, initiated and executed and managed. The rise and growth of firms such as Uber, Airbnb and Alibaba.com are strongly attributed to the advancements in digital technologies (Tan et al. 2016). Such examples epitome characteristics of digital technologies and platforms like accessibility, availability, ease-of-use and ease-of-deployment which purport to transform the very nature of how companies innovate using modern digital technologies and how entrepreneurship is facilitated.

Herein, digital technologies have provided the firms with low capital intensity, an opportunity to innovate in a similar fashion as their resourceful counterparts (Tan et al. 2016), challenging the traditional equation of IT sophistication and resource availability (Dobbs et al. 2015; Nylén and Holmström 2015). Moreover, the innovation potential of firms is said to have been augmented by the substantial growth in consumerization of IT, through which technologies have become accessible to average citizens as a commodity (Harris et al. 2012; Weiß and Leimeister 2012).

Overall, the opportunities for digital business transformation through digital technologies for innovation and entrepreneurship purport to provide unique opportunities to organizations of all sizes, regardless of their resources, geographical constraints and organizational maturity.

However, despite the proliferation, availability, accessibility, scalability and affordability of digital technologies over the past several years, firms are still struggling to reap the full innovation and entrepreneurial potential, where new ideas still do not reach the customer due to lack of organizational readiness and lack of knowledge of the organizational strategy (Snyder-Halpern 2001; Williams 2011).

As such, more research is necessary to disentangle the intricate relationship between IT with innovation and entrepreneurship in order to comprehend how new businesses may emerge alongside technological innovations. This special issue provides an opportunity for deliberation on a broad range of topics associated with recent trends in IT innovation and entrepreneurship. Over the last decade, there is a growing research stream on IT entrepreneurship in the information systems community. IT entrepreneurship adds a further dimension to IT innovation in that it deals with how original ideas can be converted into software and hardware products and services.

Accordingly, this special issue serves as a forum for focused discussion and exchange on IT innovation and entrepreneurship. We endeavour to address crucial fundamental question of the role of digital technologies in innovation and entrepreneurship in the dynamic economic realities. The special issue is open to all methodological approaches. We especially welcome papers that identify and address knowledge gaps in (but not limited to):

- challenges and opportunities associated with leveraging IT-driven innovation activities and processes for entrepreneurship,

- effects of culture on IT innovation and entrepreneurship,

- entrepreneurial attitudes toward and motives for IT innovation,

- impact of IT entrepreneurship on for individuals, businesses, and society,

- inter-firm collaboration in IT innovation and entrepreneurship,

- novel business models anchored on IT innovation,

- relationship between emerging technologies (e.g., big data analytics, blockchain, sharing economy, social media) and entrepreneurship,

- value creation and capturing through IT innovation,

- any other topic that touch on matters related to the intersection between IT innovation and entrepreneurship.

计算机体系结构,并行与分布式计算

Journal of Systems Architecture

Special Issue on Edge Computing for Internet of Things

全文截稿: 2018-09-30

影响因子: 0.913

CCF分类: B类

中科院JCR分区:

  • 大类 : 工程技术 - 4区

  • 小类 : 计算机：硬件 - 4区

  • 小类 : 计算机：软件工程 - 4区

网址: http://www.journals.elsevier.com/journal-of-systems-architecture/

With the rapid proliferation of Internet of Things (IoT) in diverse application domains, we have entered the era of big data, where huge volumes of data are collected by IoT nodes and processed either online or offline. For online data processing and analytics, real-time performance and Quality of Service requirements are paramount, and pose severe challenges to the centralized cloud computing paradigm. Although cloud computing provides tremendous computing power for big data processing, network bandwidth is often the performance bottleneck if large amounts of data are transmitted to and from the cloud. To alleviate the performance problems caused by remote network access in cloud computing, edge computing is gaining increasing importance in recent years, which aims to bring processing from a centralized cloud to distributed edge devices close to the users. Performing partial or full calculations at the edge helps reduce network latency, provide real-time response, and enhance security/privacy.

We solicit original and unpublished papers on the following topics of interest, all in the context of Edge Computing for IoT:

- Wireless communication and networking

- Resource management and scheduling algorithms

- Real-time Big Data analytics, processing and storage

- Machine learning/deep learning on edge and IoT devices

- Quality of Service guarantees

- Power and energy efficiency

- Security and privacy

- Resilience and failure-tolerance

- Modeling and implementation

- Monitoring and diagnosis

- Prototyping techniques and testbeds

计算机体系结构,并行与分布式计算

Future Generation Computer Systems

TDLSG@VLDB’2018 workshop on Advances in Managing and Mining time varying and highly dynamic graphs at scale

全文截稿: 2018-11-30

影响因子: 4.639

CCF分类: C类

中科院JCR分区:

  • 大类 : 工程技术 - 2区

  • 小类 : 计算机：理论方法 - 2区

网址: http://www.journals.elsevier.com/future-generation-computer-systems/

The aim of this special issue on Advances on Managing ang Mining Large-Scale Time Dependent Graphs (TD-LSG) is to bring together active scholars and practitioners of dynamic graphs. Graph models and algorithms are ubiquitous of a large number of application domains, ranging from transportation to social networks, semantic web, or data mining. However, many applications require graph models that are time dependent. For example, applications related to urban mobility analysis employ a graph structure of the underlying road network. Indeed, the nature of such networks are spatiotemporal. Therefore, the time a moving object takes to cross a path segment typically depends on the starting instant of time. So, we call time-dependent graphs, the graphs that have this spatio-temporal feature.

In this special issue, we aim to discuss the problem of mining large-scale time-dependent graphs, since there are many real world applications deal with a large volumes of spatio-temporal data (e.g. moving objects trajectories). Managing and analyzing large-scale time-dependent graphs is very challenging since this requires sophisticated methods and techniques for creating, storing, accessing and processing such graphs in a distributed environment, because centralized approaches do not scale in a Big Data scenario.

Contributions will clearly point out answers to one of these challenges focusing on large-scale graphs.

Aims and Scope :

Many research questions related to mining large scale time-dependent graphs, will be at the heart of this special issue such as:

1. How to build a TD-LSG using spatio-temporal data or temporal traces in general, such as to favor the mining process ?

2. How to inter-link and enrich TD-LSG with semantic resources during the mining process ?

3. How to allow scalable mining tasks over a TD-LSG ?

4. How to organize and maintain a TD-LSG in distributed architecture, such as to scale the mining process ??

The special issue aims at bringing together scholars and practitioners active in dynamic graphs, to present their research, share their knowledge and experiences, and discuss the current state of the art and the future improvements.

Topics :

We encourage papers with important new insights and experiences on knowledge discovery aspects with dynamic and evolving graphs. Those contributions should shed light on one of the questions mentioned above, related to the knowledge discovery process. Topics of interest include, but are not limited to, the following inter-linked topics, with regards to mining process:

- Theoretical foundation of TD-LSG

- Construction and maintenance of TD-LSG

- Data quality in TD-LSG

- Data integration in TD-LSG

- Indexing techniques for TD-LSG

- Distributed algorithms & navigational query processing

- TD-LSG data mining: frequent pattern mining, similarity, cluster analysis, predictive learning

- Trajectory mining in TD-LSG

- Probabilistic TD-LSG

- Applications related to TD-LSG

数据库管理与信息检索

Information Sciences

Special Issue on Current Trends of Granular Data Mining for Biomedical Data Analysis

全文截稿: 2018-12-31

影响因子: 4.305

CCF分类: B类

中科院JCR分区:

  • 大类 : 工程技术 - 2区

  • 小类 : 计算机：信息系统 - 1区

网址: http://www.journals.elsevier.com/information-sciences/

Biomedical data presents several challenges in data analysis, including high dimensionality, class imbalance and low numbers of samples. Although current research in this field has shown promising results, several research issues still need to be explored. Biomedical data are available in different formats, including numeric, textual reports, signals and images, and the data are available from different sources. The data often suffer from incompleteness, uncertainty and vagueness, which complicates conventional techniques of data mining ranging from the model, algorithm, system and application. An interesting aspect is to integrate different data sources in the biomedical data analysis process, which requires exploiting the existing domain knowledge from available sources. There is also a need to explore novel data mining methods in the biomedical research to improve predictive performance along with interpretation.

In the past, the evolution of research interest has focused on a relatively new area—granular computing (GrC), based on technologies such as fuzzy sets and rough sets. GrC provides a powerful tool for multiple granularity and multiple-view data analysis, which is of vital importance for understanding data driven analysis at different granularity levels. Biomedical data often contain a significant amount of unstructured, uncertain and imprecise data. GrC exhibits some strong capabilities and advantages in intelligent data analysis, pattern recognition, machine learning, and uncertain reasoning for biomedical data. GrC aims to find a suitable level of granularity of a given problem which can be adjusted according to the degree of fuzziness of the given problem. How to integrate GrC and data mining to combine their advantages is an interesting and important research topic. Granular Data Mining (GDM) is proposed to address this issue. Granular computing extracts knowledge from insufficient data, which can then be used in data mining for a new task/domain with big data.

Data mining based on granular computing in biomedical data analysis is an emerging field which crosses multiple research disciplines and industry domains. A vast number of real-world problems can be tackled using techniques encompassed in GrC. GDM research explores the advantages, and also challenges, derived from collecting and mining vast amounts of biomedical data.

The aims of this Special Issue in Information Sciences are: (1) to present the state-of-the-art research on granular data mining and its application in biomedical data, and (2) to provide a forum for researchers to discuss the latest progress, new research methodologies, and potential research topics.

The topics of this special issue include, but are not limited to:

- Fuzzy set theory and application in biomedical data

- Rough set theory and application in biomedical data

- Fuzzy-rough data mining and rough-fuzzy data mining

- Bio-inspired rough set and bio-inspired fuzzy rough set approaches

- Fuzzy clustering technique for biomedical data

- Novel/emerging forms of granular data mining

- Granular computing framework for big data analytic

- Granular data mining for feature learning, classification, regression, and clustering

- Granular data mining for multi-task modeling, multi-view modeling and co-learning

- Granular computing theory for biomedical applications

- Granular fuzzy set algorithm and application in biomedical data

- Granular rough set algorithm and application in biomedical data

- Fuzzy knowledge retrieval of medical images

- Granular data mining for large-scale image and multimedia processing

- Granular data mining for brain-machine interfaces and medical signal analysis

- Biomedical image mining, and video analysis

- Application of fuzzy data processing technology in large-scale healthcare data

数据库管理与信息检索

The Journal of Strategic Information Systems

Call for Proposals for Review Articles

摘要截稿: 2018-07-01

全文截稿: 2019-01-15

影响因子: 4.313

CCF分类: C类

中科院JCR分区:

  • 大类 : 工程技术 - 2区

  • 小类 : 计算机：信息系统 - 2区

网址: http://www.journals.elsevier.com/the-journal-of-strategic-information-systems

As indicated last year, The Journal of Strategic Information Systems is dedicating the June issue of JSIS each year to review articles in the realm of strategic information systems, broadly defined. The first Review Special Issue will appear in the June 2019 issue of the journal.

We are now inviting authors to submit proposals for the 2020 Review Issue of JSIS. We seek high-impact scholarly surveys of important research literatures in the strategic information systems domain. Such articles will provide a synthesis of recent research and highlight important directions for future inquiries. The Review Issue is open to proposals concerning established and emerging topics in the field of strategic information systems, including those dealing with research methods relevant for our context.

Proposals should be submitted between June 1, 2018 and July 1, 2018 via JSIS’s online submission portal at: https://www.evise.com/profile/#/JSIS/login. (Please be sure to select “Review Issue:2020” as the submission type.).

Please note that proposals may not be submitted until June 1, 2018.

Proposals should be double-spaced and include no more than ten pages of text. References, tables, and appendices do not count against this page limit. All proposals will be subject to editorial review. Please do not send complete papers. If you already have a draft of your paper, please note that in your proposal.  

Submissions will be evaluated with regard to the following criteria:

- Relevance. The proposed manuscript should thoroughly review a significant and important research area within the field of Information Systems that has strategic impact and relevance. We also welcome reviews of important research areas that have yet to make a major impact in the field of Information Systems but argumentation can be made that the area is of strategic significance to the information systems field. Obviously, it is upon the authors to make a strong linkage of the research area to the IS system phenomena.

- Scope of Interest. While papers must contribute to the strategic information systems literature and its developing agenda they must speak to scholars in cognate IS domains and fields, including, for example: Strategic Management, Organization Studies and Knowledge Management. Ethical, policy and societal issues can form an important feature.

- Viability. The proposal should clearly feasible given the tight time constraints in place. More detail on the timeline is provided below.

- Organization and Coherence. The proposal should follow a logical structure, read clearly, and thoroughly and comprehensively represent extant research in the topic area concerned.

- Insight for Future Work. The proposal should convey important implications for future research, both in the context of the chosen topic and for the strategic information systems domain more broadly.

- Timeliness/Contribution. Reviews should normally be on topics for which no recent reviews exist and the proposal should clearly indicate its intended contribution to knowledge. If existing reviews exist, they need to be identified and there needs to be compelling argumentation how the proposed review will contribute to the literature beyond the existing reviews.

计算机体系结构,并行与分布式计算

Future Generation Computer Systems

Special Issue on Social and Intelligent Applications for Future Cities

全文截稿: 2019-01-30

影响因子: 4.639

CCF分类: C类

中科院JCR分区:

  • 大类 : 工程技术 - 2区

  • 小类 : 计算机：理论方法 - 2区

网址: http://www.journals.elsevier.com/future-generation-computer-systems/

In the last few years we have observed an increasing presence of intelligent applications in our daily lives: from accurate product recommendations, cyber-threat detection, to sophisticated software assistants. Many of these applications have certainly had an impact in our lifestyle, but they have mostly remained in the realm of the digital world. Despite the fact that we are increasingly digital citizens, cities and urban areas will always be our main ecosystem and, therefore, the main aspect affecting our daily lives.

The next barrier for intelligent applications is pervading urban areas to optimize resources, foster sustainable practices, fighting inequalities, creating new opportunities, and, generally, improving the welfare of their inhabitants. While the goals seem attractive, they do not come without their challenges: harnessing the predictive power behind humungous volumes of data generated by citizens and deployed sensors, managing and distributing scarce and limited resources, providing scalable intelligent solutions that support millions of users, fostering cooperation and coordination between thousands of self-interested agents, using human relationships and interactions to the cities’ own advantage, and effectively interacting with a wide range of citizens.

This special issue aims to advance the state of the art in the application of intelligent methods to improve cities and urban areas, with a special focus on those applications that take into consideration human collaboration, interactions, communication, or social behavior. We specially encourage the submission of articles describing applications, but we also welcome theoretical work and review articles on novel applications of intelligent methods to cities.

Topics of interest include, but are not limited to:

- Multi-Agent Systems

- Big Data

- Machine learning

- Sensors and actuators

- Smart homes and smart buildings

- Smart health

- Smart mobility and transportation

- Crowdsourcing

- Semantic Web, Linked Data

- Open Data, Linked Open Data

- Social Networks, Virtual Communities

- Social simulation

- Internet of Things

- Smart cities

计算机体系结构,并行与分布式计算

Future Generation Computer Systems

Special Issue on FinTech Security and Privacy

全文截稿: 2019-02-01

影响因子: 4.639

CCF分类: C类

中科院JCR分区:

  • 大类 : 工程技术 - 2区

  • 小类 : 计算机：理论方法 - 2区

网址: http://www.journals.elsevier.com/future-generation-computer-systems/

Advances in the capability of data computing and processing have ignited an explosion in paradigm, driving Financial Technology (FinTech) forward at an ever-accelerating rate with unprecedented new financial services. The result is that FinTech is now widely perceived as the next phase in the evolution of financial services, in which financial affairs and technology are seamlessly integrated. It has become clear that established financial institutions will need to continue driving innovation and meeting consumer needs while simultaneously satisfying new regulatory requirements. In light of the rise in interest around FinTech, both the research community and industry must intensify the attention given to overcoming the trust, security and privacy challenges germane to FinTech to unleash its full potential. There is currently no consensus on best practices regarding how FinTech can be applied with robust security and privacy preservation. This special issue invites original research that investigates issues related to FinTech security and privacy. Potential topics include, but are not limited to, the following:

- Accountability for FinTech

- Authentication for FinTech

- Anonymity for FinTech

- Blockchain-based solutions for FinTech security and privacy

- Cross-platform malware prevention for FinTech

- Management for data ubiquity, data sharing and data ownership

- New cryptographic algorithms for FinTech

- New framework for FinTech security and privacy

- Fraud detection and forensics for FinTech

- Trust management for FinTech

数据库管理与信息检索

Advanced Engineering Informatics

Special Issue of Advanced Engineering Informatics: Big Data Analytics in Construction Management

全文截稿: 2019-03-01

影响因子: 3.358

CCF分类: B类

中科院JCR分区:

  • 大类 : 工程技术 - 3区

  • 小类 : 计算机：人工智能 - 3区

  • 小类 : 工程：综合 - 2区

网址: http://www.journals.elsevier.com/advanced-engineering-informatics

As recent construction projects become complex and require knowledge-intensive tasks, capturing and analysing ‘big data’ is essential in the construction management domain to discover new knowledge and make better engineering decision-makings. Big data analytics using advanced computing methods can fulfill the needs by facilitating the generation of new knowledge and insights into the designs and processes of construction management. Because of its significance, there have been growing research efforts in advancing the current construction management systems based on big data analytics. This special issue strives to highlight current development and applications of big data analytics in construction management.

This special issue welcomes high quality papers that present 1) data-intensive analysis solutions for discovering knowledge and information from construction big data; and/or 2) computational tools for visualising and representing the captured knowledge in an intuitive and easy to understand manner, which can be useful for formulating decisions and converting data into actionable knowledge. It is expected that this special issue would be a critical milestone that presents future directions of big data analytics in the construction management domain. At the same time, it also will provide industry with some useful insights and approaches to develop practical solutions.

The topics of the special issue include, but are not limited to the followings:

- Big data for building/civil infrastructure design management

- Big data for construction safety management

- Big data for construction progress and quality management

- Big data for facility maintenance and management

- Big data with Internet of Things (IOT) for construction management

- Big data hardware/software solutions for construction management

- Machine learning and data mining applications for construction management

- Trends and strategies in big data analytics for construction management

- Real-world applications of big data analytics for the architecture, engineering and construction (AEC) industry

计算机体系结构,并行与分布式计算

Journal of Parallel and Distributed Computing

Special Issue on Virtualization for Future Computing Systems (Future Virtualization 2019)

全文截稿: 2019-03-31

影响因子: 1.815

CCF分类: B类

中科院JCR分区:

  • 大类 : 工程技术 - 3区

  • 小类 : 计算机：理论方法 - 3区

网址: http://www.journals.elsevier.com/journal-of-parallel-and-distributed-computing/

SCOPE

Virtualization is a widely used technology intended to enable efficient management of resources in computing systems that range from small desktops and clusters to large facilities such as Cloud data centers. This technology increases the utilization of the underlying hardware by offering multi-tenancy. Furthermore, isolation among multiple users on the same resource is also provided, which is a mechanism for effective deployment of workloads. On the other hand, virtualization is a platform to deal with heterogeneity of resources. Consequently, the virtualization technology impacts the entire computing stack-the hardware, middleware and application layers. Rapid advances in hardware acceleration, the drive towards efficiently achieving exascale computing, harnessing the use of multiple and decentralized Clouds and opportunities in decoupling physical and logical networks is paving way for significant innovation in the virtualization arena. Virtualization may be used in many other domains and for many other purposes, some of them not even thought yet.

This special issue invites authors to submit original and innovative research articles that impact any avenue of virtualization for future computing systems.

TOPICS OF INTEREST

Topics of interest include all avenues of virtualization that affects future computing systems, but are not limited to:

- Platforms: Desktops, Clouds, Supercomputers, High-Performance Computing (HPC) clusters, micro data centers; for upcoming Fog/Edge computing and Internet-of-Things platforms

- Hardware: Processors, accelerators (GPUs, FPGAs, etc), memory, IO, network (SDN, NFV etc), storage

- Middleware: Hypervisor support for the above; Abstraction of heterogeneous resources; Extending existing functionalities; Novel API Remoting and Hypervisor abstraction approaches in accelerator virtualization; Scheduling of virtualized resources; Multi-tenancy and its benefits

- Technologies: Virtual machines, Containers, Unikernels; Frameworks dealing with any aspect of management, such as scheduling, live migration and orchestration; Support for multi-kernel approaches; Lightweight virtualization for resource constrained environments

- Systems: Programming languages and models to support virtualization; Approaches relying on virtualization for improving reliability, energy-efficiency, latencies, resource under-utilization, resource availability, application performance and fault tolerance

- Applications: Use of virtualization for traditional workloads on any platforms indicated above; Novel workloads in deep learning, mobile apps, IoT, smart cities, etc that rely on virtualization

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