数据处理 | 会议/期刊约稿信息7条

数据库管理与信息检索

Journal of Web Semantics

Special Issue on Representation Learning for the Semantic Web

全文截稿: 2017-11-30

影响因子: 1.075

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 : 计算机：信息系统 - 3区

  • 小类 : 计算机：软件工程 - 2区

网址: http://www.journals.elsevier.com/journal-of-web-semantics

The Journal of Web Semanticsinvites submissions for a special issue on representation learning for the Semantic Web, to be edited by Heiko Paulheim, Volker Tresp, and Zhiyuan Liu. Submissions are due by November 30th, 2017.

In the past years, learning vector space embeddings has rapidly gained attention, first in the natural language processing community with the advent of word2vec, and more recently also in the Semantic Web community, e.g., with the adaptations RDF2vec or node2vec, as well as the RESCAL/HolE and Trans* family. Their properties - the representation of entities in a dense vector space, the proximity of semantically related entities, and the preservation of the direction of semantic relations - make them interesting for many applications.

There are various ways of creating such embeddings. They range from applying the word2vec paradigm to sequences derived from graphs to translation learning and tensor factorization. Those methods differ both in their scalability on different types of input datasets, as well as in the characteristics of the resulting embeddings.

At the same time, novel use cases for embeddings of Semantic Web data are discussed. Those applications range from Semantic Web specific use cases, such as link prediction in knowledge graphs, to general applications, such as recommender systems and decision support systems. In many of those fields, approaches leveraging embeddings have recently been reported to outperform traditional techniques.

The aim of this special issue is to present latest advances in neural embeddings for the Semantic Web, as well as novel applications. Topics of submissions include, but are not limited to:

- Novel methods for learning embeddings

- Theory of representation learning for the Semantic Web

- Embeddings of ontologies and knowledge graphs

- Joint embeddings of Semantic Web and non-Semantic Web data (e.g., text, media, ...)

- Paradigms for sharing and reusing embeddings

- Embedding systems for modeling human memories

- Scalability of embedding learning

- Implementations

- Reusable embeddings for popular Semantic Web resources, e.g., DBpedia or Wikidata

- Software frameworks for learning and using embeddings

- Experimental studies and benchmarks

- Application areas of embeddings, e.g., recommender systems, entity search, or named entity disambiguation

数据库管理与信息检索

ALLDATA 2018

International Conference on Big Data, Small Data, Linked Data and Open Data

全文截稿: 2017-12-04

开会时间: 2018-04-22

会议难度: ★

CCF分类: 无

会议地点: Athens, Greece

网址：http://iaria.org/conferences2018/ALLDATA18.html

ALLDATA 2018 conference tracks:
-Challenges in processing Big Data and applications
-Big Data
-Small Data
-Linked Data
-Open Data

数据库管理与信息检索

CiA 2018

Workshop on Context in Analytics

全文截稿: 2017-12-15

开会时间: 2018-04-16

会议难度: ★★

CCF分类: 无

会议地点: Paris, France

网址：https://umddb.github.io/contextanalytics2018/

As the role of computers in decision-making expands in business, education, health care, law, and government, big data and analytics community needs to pay specific attention to context. Studying, representing, incorporating, exploiting and explaining context in analytics has the potential to provide significant value and address major challenges.

The 1st Workshop on Context in Analytics (CiA) encourages participation from a broad spectrum of communities to explore the role of context in big data and analytics, as we collectively try to address questions such as: What is the right model of context? What should context capture? What is the architecture of context services? How to address data integration when bringing in context from various systems? Can context provide value without violating privacy? What are the right user experience and interaction models for interaction with context? How should context be governed?

数据库管理与信息检索

DBKDA 2018

International Conference on Advances in Databases, Knowledge, and Data Applications

全文截稿: 2018-01-03

开会时间: 2018-05-20

会议难度: ★★

CCF分类: 无

会议地点: Nice, France

网址：http://iaria.org/conferences2018/DBKDA18.html

DBKDA 2018 is colocated with the following events as part of InfoSys 2018.

DBKDA 2018 conference tracks:
-Advances in fundamentals on databases
-Current ongoing researches
-Databases and other domains
-Databases technologies
-Databases content processing
-Knowledge and decision bases
-Specifics on application domains databases
-XML-driven data, knowledge, databases
-Data privacy
-Data quality and uncertainty
-Data query, access, mining, and correlation
-Data and process provenance
-Data management

数据库管理与信息检索

Information Sciences

Special Issue on Distributed Event-Triggered Control and Estimation in Resource-Constrained Cooperative Networks

全文截稿: 2018-01-15

影响因子: 4.832

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 - 2区

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

In many practical applications, such as environmental monitoring systems, smart grids, intelligent transportation systems, and wireless robotics, there is a fundamental need to accomplish coordinated tasks, such as consensus estimation, agreement at a common point or move in an anticipated formation pattern, across time and space that cannot be achieved by a single node or agent. The ever-increasing demand for cooperative networks of nodes or agents has stimulated widespread research interest in developing distributed estimation and control strategies that guarantee coordinated tasks. The benefits of employing cooperative networks lie in several aspects including flexibility, manipulability and scalability that are beyond the capability of an individual node or an agent. However, in the context of distributed estimation and control, nodes or agents usually possess limited sensing, computing and communication capabilities, and network bandwidth sometimes may also be restricted. These constraints pose significant challenges to the analysis and design of resource-constrained cooperative networks. Thus, it is of great significance in both theory and practice to regulate the sampling, communication or actuation frequencies among interacting nodes or agents such that over-utilization of the available computation and communication resources can be effectively reduced while preserving desired estimation and control performance for such cooperative networks. Event-triggered mechanisms, which abandon the conventional periodic updating of task commands, have been proven to be an effective and promising tool capable of alleviating network bandwidth occupancy and reducing computation and communication cost.

This special issue aims at advancing the event-triggered technology and methodology and further promote the research activities in distributed event-triggered estimation and control for cooperative networks, such as sensor networks, complex networks and multi-agent networks. The special issue seeks original work to address some emerging issues and challenges from distributed event-triggered estimation and control and their applications to areas, such as power systems, robotics, vehicular networks, and camera networks.

Topics of interest include but not limited to:[WP1]

- Distributed event-triggered control and estimation

- Event-triggered control of networked control systems

- Event-triggered estimation of networked systems

- Consensus and diffusion estimation of sensor networks

- Distributed event-triggered estimation of complex networks

- Synchronization and pinning control of complex networks

- Consensus of multi-agent systems

- Formation control of multi-agent systems

- Containment control of multi-agent systems

- Applications of event-triggered control and estimation in smart grids, wireless robotics, unmanned aerial vehicles, autonomous underwater vehicles, vehicular networks, and camera networks and so on

数据库管理与信息检索

Information Sciences

Special Issue on Business Analytics – Emerging Trends and Challenges

全文截稿: 2018-03-30

影响因子: 4.832

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 - 2区

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

We are living in a world characterized by an abundance of many different kinds of data. The importance of analyzing information contained therein has been already recognized by academia and practitioners. As a result, we have witnessed a rapidly growing number of products and solutions by the respective solution providers.

What is still missing, however, is an adequate interpretation and modeling of the ever growing data sets to create intelligent systems that are able to propose advanced solutions to complex problems.

As a consequence, this special issue aims at moving towards the next step and foster the development of advanced techniques coming from machine learning, artificial intelligence, uncertainty modeling, and data science, among others, to establish emerging trends in business analytics capable of facing the current challenges. In this special issue we understand the term “Business Analytics” in a rather broad sense, covering a spectrum of different application areas.

Topics relevant for this special issue include, but are not limited to:

- Business Analytics for Financial Modeling

- Forecasting

- Human Resources (HR) Analytics

- Healthcare Analytics

- Learning Analytics

- Fraud Detection and Cybersecurity

- Privacy-preserving and ethics in Business Analytics

数据库管理与信息检索

Information Sciences

Special Issue on Privacy Computing: Principles and Applications

全文截稿: 2018-05-30

影响因子: 4.832

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 - 2区

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

While more and more data including personal information is being hosted online such as cloud infrastructure, privacy leakage is becoming one of most challenging concerns in information collection, sharing or analysis. In practice, different temporal, spatial or application cases often demand different privacy protection solutions. Accordingly, most of traditional approaches are case by case or based on a specific application circumstance. It is on demand for a systematic and quantized privacy characterization towards systematic computing model describing the relationships between protection level, profit and loss as well as the complexity of integrated privacy protection models because real-world applications with privacy are changing across time, space and different domains. This special issue focuses on the new paradigm of privacy computing for principles and applications. High quality publications are solicited from engineers and scientists in academia, industry, and government to address the resulting profound challenges on principles and applications of privacy computing.

Topics of interests include, but are not limited to:

- Fundamental principles for privacy computing

- Privacy principles for information sensing, collection, engineering and distribution

- Privacy protection based information hiding and sharing principles

- Privacy preserving data publishing principles

- Privacy information integration, synergy and storage

- Privacy operation and modelling methodologies

- Privacy protection methodologies and principles

- Privacy applications in cloud, social networks, IoT and Industrial Internet

- Privacy, security, trust, autonomy, reliability, fault-tolerance – association principles

- Privacy, AI, Machine Learning, Data Mining and Knowledge Discovery – association principles