A Study On Similarity Join In Heterogeneous Information Network Using Path Based Algorithm

Download or read book A STUDY ON SIMILARITY JOIN IN HETEROGENEOUS INFORMATION NETWORK USING PATH BASED ALGORITHM written by S. Sandhya and published by Archers & Elevators Publishing House. This book was released on with total page 75 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Web Information Systems and Applications written by Weiwei Ni and published by Springer Nature. This book was released on 2019-09-17 with total page 725 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 16th International Conference on Web Information Systems and Applications, WISA 2019, held in Qingdao, China, in September 2019. The 39 revised full papers and 33 short papers presented were carefully reviewed and selected from 154 submissions. The papers are grouped in topical sections on machine learning and data mining, cloud computing and big data, information retrieval, natural language processing, data privacy and security, knowledge graphs and social networks, blockchain, query processing, and recommendations.

Download or read book Mining Heterogeneous Information Networks written by Yizhou Sun and published by Morgan & Claypool Publishers. This book was released on 2012 with total page 162 pages. Available in PDF, EPUB and Kindle. Book excerpt: Investigates the principles and methodologies of mining heterogeneous information networks. Departing from many existing network models that view interconnected data as homogeneous graphs or networks, the semi-structured heterogeneous information network model leverages the rich semantics of typed nodes and links in a network and uncovers surprisingly rich knowledge from the network.

Download or read book Modern Principles, Practices, and Algorithms for Cloud Security written by Gupta, Brij B. and published by IGI Global. This book was released on 2019-09-27 with total page 361 pages. Available in PDF, EPUB and Kindle. Book excerpt: In today’s modern age of information, new technologies are quickly emerging and being deployed into the field of information technology. Cloud computing is a tool that has proven to be a versatile piece of software within IT. Unfortunately, the high usage of Cloud has raised many concerns related to privacy, security, and data protection that have prevented cloud computing solutions from becoming the prevalent alternative for mission critical systems. Up-to-date research and current techniques are needed to help solve these vulnerabilities in cloud computing. Modern Principles, Practices, and Algorithms for Cloud Security is a pivotal reference source that provides vital research on the application of privacy and security in cloud computing. While highlighting topics such as chaos theory, soft computing, and cloud forensics, this publication explores present techniques and methodologies, as well as current trends in cloud protection. This book is ideally designed for IT specialists, scientists, software developers, security analysts, computer engineers, academicians, researchers, and students seeking current research on the defense of cloud services.

Download or read book Network Embedding written by Cheng Yang and published by Morgan & Claypool Publishers. This book was released on 2021-03-25 with total page 244 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is a comprehensive introduction to the basic concepts, models, and applications of network representation learning (NRL) and the background and rise of network embeddings (NE). It introduces the development of NE techniques by presenting several representative methods on general graphs, as well as a unified NE framework based on matrix factorization. Afterward, it presents the variants of NE with additional information: NE for graphs with node attributes/contents/labels; and the variants with different characteristics: NE for community-structured/large-scale/heterogeneous graphs. Further, the book introduces different applications of NE such as recommendation and information diffusion prediction. Finally, the book concludes the methods and applications and looks forward to the future directions. Many machine learning algorithms require real-valued feature vectors of data instances as inputs. By projecting data into vector spaces, representation learning techniques have achieved promising performance in many areas such as computer vision and natural language processing. There is also a need to learn representations for discrete relational data, namely networks or graphs. Network Embedding (NE) aims at learning vector representations for each node or vertex in a network to encode the topologic structure. Due to its convincing performance and efficiency, NE has been widely applied in many network applications such as node classification and link prediction.

Download or read book Web and Big Data written by Xin Wang and published by Springer Nature. This book was released on 2020-10-15 with total page 829 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two-volume set, LNCS 11317 and 12318, constitutes the thoroughly refereed proceedings of the 4th International Joint Conference, APWeb-WAIM 2020, held in Tianjin, China, in September 2020. Due to the COVID-19 pandemic the conference was organizedas a fully online conference. The 42 full papers presented together with 17 short papers, and 6 demonstration papers were carefully reviewed and selected from 180 submissions. The papers are organized around the following topics: Big Data Analytics; Graph Data and Social Networks; Knowledge Graph; Recommender Systems; Information Extraction and Retrieval; Machine Learning; Blockchain; Data Mining; Text Analysis and Mining; Spatial, Temporal and Multimedia Databases; Database Systems; and Demo.

Download or read book Expertise Retrieval written by Krisztian Balog and published by Foundations and Trends(r) in I. This book was released on 2012-07 with total page 146 pages. Available in PDF, EPUB and Kindle. Book excerpt: People have looked for experts since before the advent of computers. With advances in information retrieval technology, coupled with the large-scale availability of traces of knowledge-related activities, computer systems that can fully automate the process of locating expertise have become a reality. The past decade has witnessed tremendous interest and a wealth of results in expertise retrieval as an emerging subdiscipline in information retrieval. This survey highlights advances in models and algorithms relevant to this field. We draw connections among methods proposed in the literature and summarize them in five groups of basic approaches. These serve as the building blocks for more advanced models that arise when we consider a range of content-based factors that may impact the strength of association between a topic and a person. We also discuss practical aspects of building an expert search system and present applications of the technology in other domains such as blog distillation and entity retrieval. The limitations of current approaches are also pointed out. We end our survey with a set of conjectures on what the future may hold for expertise retrieval research.