检索结果-内蒙古大学图书馆

A Tutorial on Internet of behaviors: Concept, Architecture, Technology, Applications, and Challenges

IEEE COMMUNICATIONS SURVEYS AND TUTORIALS 2023年第2期25卷 1227-1260页

作者： Zhao, Qinglin Li, Guangcheng Cai, Jincheng Zhou, MengChu Feng, Li Macau Univ Sci & Technol Sch Comp Sci & Engn Macau Peoples R China Zhejiang Gongshang Univ Sch Informat & Elect Engn Hangzhou 314423 Peoples R China New Jersey Inst Technol Helen & John C Hartmann Dept Elect & Comp Engn Newark NJ 07102 USA

In his blogs of 2012, Dr. Gote Nyman coined Internet of behaviors (IoB). In his idea, people's behaviors are very good predictors of their needs, and hence technology companies can network human behaviors and construct behavior-based systems to deliver more timely, responsive, and intelligent services with privacy protection. In 2020, Gartner ranked IoB first among its top nine strategic technology trends and suggested that IoB would touch almost half of the world population. Built on his core idea of IoB, this paper is the first one to comprehensively, systematically, and deeply introduce the IoB concept, essential features, architecture, enabling technologies, applications, and open research issues. In this paper, we first give a formal definition and classification of IoB and reveal its fundamental difference from Internet of Things (IoT). Then, we propose a five-layer IoB architecture (consisting of behavior perception, behavior networking, behavior computing, service provision, and security/privacy) and provide the in-depth analysis of IoB enabling technologies. Particularly, we discuss functional requirements and possible fields of an IoB address (i.e., a human behavior identifier), present the networking and maintenance approaches to behaviors, explore four important implications of behavior computing, i.e., intention inference, behavior derivation, behavior programming, and behavior-chain optimization, as well as give a decentralized privacy-protection solution to Dr. Nyman's behavior-identity-separation idea. Next, we deeply investigate potential IoB applications in smart home/transportation/healthcare/business, and human-robot interaction. Finally, we provide insightful discussions on open research issues. This paper should help researchers and practitioners understand IoB quickly and promote future IoB development.

关键词： Internet of behaviors (IoB) Internet of Things (IoT) behavior perception behavior networking behavior computing architecture applications security and privacy

来源：评论

学校读者我要写书评

暂无评论

behavior Informatics to Discover behavior Insight for Active and Tailored Client Management 17

Behavior Informatics to Discover Behavior Insight for Active...

引用

23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD)

作者： Cao, Longbing Univ Technol Sydney Adv Analyt Inst Sydney NSW Australia

ISBN: (纸本)9781450348874

behavior is ubiquitous, and behavior intelligence and insight play an important role in data understanding and business problem-solving. behavior Informatics [1,2] emerges as an important tool for discovering behavior intelligence and behavior insight. As a computational concept, behavior captures the aspects of the demographics of behavioral subjects and objects; social relationships or norms governing the interactions between behaviors of an individual or a group; behavior sequences or networks and their dynamics; and the impact or effect generated by the behaviors undertaken by subjects on objects. Accordingly, a behavior model [2] captures the subject and the object of a behavior or behavior sequence, the activities conducted by its subject on objects, and the relationships between activities; behavior subject, object, activities and relationships are characterized by their respective attributes. As a result, a behavior is represented as a behavior attributes-based vector; and a subject's behaviors at a time period form a vector-based sequence, namely, represented as a behavior attribute vector-based matrix [3]. With such behavior modeling and from the informatics perspective, behavior informatics takes a top-down approach to systematically and deeply represent, model, reason about, and aggregate behaviors [4]; and a bottom-up approach to analyze and learn behavior occurrences, non-occurrences, dynamics, impact, and utility [2].Accordingly, for a real-life problem, first, its data is converted to behavioral data according to the above behavior model, characterized by the relevant activities that form behavior sequences, and the properties of subjects, objects, activities, and relationships. Second, analytical tasks, such as behavior pattern analysis, abnormal behavior detection, coupled analysis of group behaviors, modeling of behavior impact and utility, discovery of high impact and high utility behaviors, analysis of non-occurring behaviors, and analysis of be

关键词： behavior informatics behavior computing behavior intelligence behavior insight behavior modeling user modeling coupled behavior analysis non-occurring behavior

来源：评论

学校读者我要写书评

暂无评论

e-NSP: Efficient negative sequential pattern mining

引用

ARTIFICIAL INTELLIGENCE 2016年 235卷 156-182页

作者： Cao, Longbing Dong, Xiangjun Zheng, Zhigang Univ Technol Sydney Sydney NSW 2007 Australia Qilu Univ Technol Jinan Peoples R China

As an important tool for behavior informatics, negative sequential patterns (NSP) (such as missing medical treatments) are critical and sometimes much more informative than positive sequential patterns (PSP) (e.g. using a medical service) in many intelligent systems and applications such as intelligent transport systems, healthcare and risk management, as they often involve non-occurring but interesting behaviors. However, discovering NSP is much more difficult than identifying PSP due to the significant problem complexity caused by non-occurring elements, high computational cost and huge search space in calculating negative sequential candidates (NSC). So far, the problem has not been formalized well, and very few approaches have been proposed to mine for specific types of NSP, which rely on database re-scans after identifying PSP in order to calculate the NSC supports. This has been shown to be very inefficient or even impractical, since the NSC search space is usually huge. This paper proposes a very innovative and efficient theoretical framework: set theory-based NSP mining (ST-NSP), and a corresponding algorithm, e-NSP, to efficiently identify NSP by involving only the identified PSP, without re-scanning the database. Accordingly, negative containment is first defined to determine whether a data sequence contains a negative sequence based on set theory. Second, an efficient approach is proposed to convert the negative containment problem to a positive containment problem. The NSC supports are then calculated based only on the corresponding PSP. This not only avoids the need for additional database scans, but also enables the use of existing PSP mining algorithms to mine for NSP. Finally, a simple but efficient strategy is proposed to generate NSC. Theoretical analyses show that e-NSP performs particularly well on datasets with a small number of elements in a sequence, a large number of itemsets and low minimum support. e-NSP is compared with two currently avail

关键词： Negative sequence analysis Sequence analysis behavior analytics Non-occurring behavior behavior informatics behavior computing Pattern mining

来源：评论

学校读者我要写书评

暂无评论

behavior Mining Language for Mining Expected behavior from Log Files 42

Behavior Mining Language for Mining Expected Behavior from L...

引用

42nd Annual Conference of the IEEE-Industrial-Electronics-Society (IECON)

作者： Heikkinen, Esa Hamalainen, Timo D. Tampere Univ Technol Dept Pervas Comp POB 553 Tampere 33101 Finland

ISBN: (纸本)9781509034741

Log files are often the only way to identify and locate errors in a deployed system. This paper proposes a novel behavior Mining Language (BML) for a log file analyzing framework called LOGDIG. It is proposed for logs that include temporal data (timestamps) and extra-log system-specific data (e.g. spatial data with coordinates of moving objects), which are present e.g. in Real Time Passenger Information Systems (RTPIS). BML is state machine-based, and specifies searches for desired events from the log files by adjustable accuracy. The analysis output is static behavioral knowledge and human friendly composite log files for reporting the results in legacy tools. Field data from a commercial RTPIS is used as a proof-of-concept case study. BML is Python based for excellent development support, as well as self-explanatory and self-descriptive for correct-by-construct usage. Compared to a general language approach, BML code is much shorter and easier to maintain. In the RTPIS case study we compare BML to the closest related log file analysis language LFAL2. BML can be applied to complicated cases that are not possible to capture using LFAL2, but at the penalty of more setting-up effort. Thus, BML is positioned between the simple specific and the fully general programming languages used in log file analysis. BML fulfills specifically the needs for log analysis on RTPIS kind of systems.

关键词： Log file analysis data mining spatiotemporal data mining behavior computing metalanguage DSL test oracles RTPIS Python

来源：评论

学校读者我要写书评

暂无评论

A Location-Item-Time sequential pattern mining algorithm for route recommendation

引用

KNOWLEDGE-BASED SYSTEMS 2015年 73卷 97-110页

作者： Tsai, Chieh-Yuan Lai, Bo-Han Yuan Ze Univ Dept Ind Engn & Management Taipei Taiwan Yuan Ze Univ Innovat Ctr Big Data & Digital Convergence Taipei Taiwan

To survive in a rapidly changing environment, theme parks need to provide high quality services in terms of visitor tastes and preferences. Understanding the spatial and temporal behavior of visitors could enhance the attraction management and geographical distribution for visitors. To fulfill the need, this research defined a Location-Item-Time (LIT) sequence to describe visitor's spatial and temporal behavior. Then, the Location-Item-Time PrefixSpan (LIT-PrefixSpan) mining algorithm is developed to discover frequent LIT sequential patterns. Next, the route suggestion procedure is proposed to retrieve suitable LIT sequential patterns for visitors under the constraints of their intended-visiting time, favorite regions, and favorite recreation facilities. A simplified theme park is used as an example to show the feasibility of the proposed system. The experimental results show that the system can help managers understand visitors' behavior and provide appropriate visiting experiences for visitors. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Recommendation systems Sequential pattern Sequence mining behavior computing Theme park

来源：评论

学校读者我要写书评

暂无评论

An Effective Herd behavior Detection Approach based on Density Clustering

An Effective Herd Behavior Detection Approach based on Densi...

引用

2015全国理论计算机科学学术年会

作者： Qian Li Chunsheng Tao Xv Lan Chengzhang Zhu School of Economics Minzu University of ChinaBeijing 100083China College of Computer National University of Defense TechnologyChangsha 410073China

Herd behavior is a phenomenon that often appears in the stock *** is caused by the irrational imitation of investors and is expressed as major investors make similar investing decisions in a short time *** herd behavior is necessary for both regulatory authorities and investors to reduce investment ***, many researchers focused on herd behavior detection and proposed numbers of detection *** most of these methods only can judge whether herd behavior exists in the stock market, they cannot detect which stocks are in the same herd and which stock is the leader of the *** this paper, we proposed an efficient herd behavior detection approach based on density *** firstly uses stock return rates in a period to calculate similarity of stock-pairs at that ***, it finds the leader of herds by finding local density *** that, it clusters stocks following their probable leaders and discovers herds in stock ***, it determines whether existing herd behavior and the degree of herd behavior according to the size of herds, *** number of stock in *** results on the Chinese stock market demonstrate the effectiveness and accuracy of this approach.

关键词： Herd behavior Detection Density Clustering behavior computing

来源：评论

学校读者我要写书评

暂无评论

behavior Informatics: A New Perspective

引用

IEEE INTELLIGENT SYSTEMS 2014年第4期29卷 62-66页

作者： Cao, Longbing Univ Technol Sydney Adv Analyt Inst Sydney NSW 2007 Australia Univ Technol Sydney Fac Engn & IT Sydney NSW 2007 Australia

This installment of Trends & Controversies provides an array of perspectives on the latest research in behavior informatics. Longbing Cao introduces the work in "behavior Informatics: A New Perspective." Then, in "behavior computing," Longbing Cao and Thorsten Joachims provide a basic overview of the topic. Next is "Coupled behavior Representation, Modeling, Analysis, and Reasoning" by Can Wang, Longbing Cao, Eric Gaussier, Jinjiu Li, Yuming Ou, and Dan Luo. The fourth article is "behavior Analysis in Social Media," by Reza Zafarani and Huan Liu. The fifth article is "Group Recommendation and behavior," by Guandong Xu and Zhiang Wu. Gabriella Pasi wrote the sixth article, "Web Search and behavior." The seventh article, "behaviors of IPTV Users," is by Ya Zhang, Xiaokang Yang, and Hongyuan Zha. Finally, "Should behavioral Models of Terror Groups Be Disclosed?" is by Edoardo Serra and V.S. Subrahmanian.

关键词： behavioural sciences computing behavior computing behavior informatics Analytical models behavioral science Big data Business Computational modeling Data mining Informatics Big Data behavior analysis behavior analytics behavior computing behavior informatics coupled behavior group behavior group profile group recommender systems intelligent systems social networking

来源：评论

学校读者我要写书评

暂无评论

In-depth behavior understanding and use: The behavior informatics approach

引用

INFORMATION SCIENCES 2010年第17期180卷 3067-3085页

作者： Cao, Longbing Univ Technol Sydney Fac Engn & Informat Technol Sydney NSW 2007 Australia

The in-depth analysis of human behavior has been increasingly recognized as a crucial means for disclosing interior driving forces, causes and impact on businesses in handling many challenging issues such as behavior modeling and analysis in virtual organizations, web community analysis, counter-terrorism and stopping crime. The modeling and analysis of behaviors in virtual organizations is an open area. Traditional behavior modeling mainly relies on qualitative methods from behavioral science and social science perspectives. On the other hand, so-called behavior analysis is actually based on human demographic and business usage data, such as churn prediction in the telecommunication industry, in which behavior-oriented elements are hidden in routinely collected transactional data. As a result, it is ineffective or even impossible to deeply scrutinize native behavior intention, lifecycle and impact on complex problems and business issues. In this paper, we propose the approach of behavior informatics (Bp, in order to support explicit and quantitative behavior involvement through a conversion from source data to behavioral data, and further conduct genuine analysis of behavior patterns and impacts. BI consists of key components including behavior representation, behavioral data construction, behavior impact analysis, behavior pattern analysis, behavior simulation, and behavior presentation and behavior use. We discuss the concepts of behavior and an abstract behavioral model, as well as the research tasks, process and theoretical underpinnings of BI. Two real-world case studies are demonstrated to illustrate the use of 131 in dealing with complex enterprise problems, namely analyzing exceptional market microstructure behavior for market surveillance and mining for high impact behavior patterns in social security data for governmental debt prevention. Substantial experiments have shown that BI has the potential to greatly complement the existing empirical and specific

关键词： Informatics behavior analysis behavior informatics behavior computing Decision making

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：