XML Object Identification

2014 ◽  
pp. 140-170
Keyword(s):  
Entity Resolution ◽  
Data Representation ◽  
Object Identification ◽  
Document Management ◽  
Future Research ◽  
Xml Data ◽  
Xml Document ◽  
Peer To Peer Systems ◽  
Structured Information ◽  
Algorithm Base

For the ability to represent data from a wide variety of sources, XML is rapidly emerging as the new standard for data representation and exchange on Web and e-government. To effectively use XML data in practice, entity resolution, which has been proven extremely useful in data fusion, inconsistency detection, and data repairing, must be in place to improve the quality of the XML data. In this chapter, the authors deal specifically with object identification on XML data, the application of which includes XML document management in highly dynamic applications like the Web and peer-to-peer systems, detection of duplicate elements in nested XML data, and finding similar identities among objects from multiple Web sources. The authors survey techniques of pairwise and groupwise entity resolution for XML data, which adopt structured information to describe the similarity or distance of XML data, like XML document and XML elements in document, and find the matching pairs which describe same object or classify them into separate groups, each group corresponding to the same object in real world. There are a lot of ways to describe the XML structure and content, such as a tree, Bayesian network, and set. The authors introduce some well-known algorithm base on these structures to solve matching XML data problems. Finally, the authors discuss directions for future research.

Efficient Entity Resolution on XML Data Based on Entity-Describe-Attribute

2011 ◽  
Vol 34 (11) ◽  
pp. 2131-2141 ◽  
Author(s):  
Ya-Kun LI ◽  
Hong-Zhi WANG ◽  
Hong GAO ◽  
Jian-Zhong LI
Keyword(s):  
Entity Resolution ◽  
Xml Data

scholarly journals Keyword Search over Probabilistic XML Documents Based on Node Classification

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Yue Zhao ◽  
Ye Yuan ◽  
Guoren Wang
Keyword(s):  
Keyword Search ◽  
Possible World ◽  
Xml Data ◽  
Fast Learning ◽  
Probabilistic Xml ◽  
Learning Speed ◽  
Xml Document ◽  
Probability Threshold ◽  
Node Classification ◽  
Learning Machine

This paper describes a keyword search measure on probabilistic XML data based on ELM (extreme learning machine). We use this method to carry out keyword search on probabilistic XML data. A probabilistic XML document differs from a traditional XML document to realize keyword search in the consideration of possible world semantics. A probabilistic XML document can be seen as a set of nodes consisting of ordinary nodes and distributional nodes. ELM has good performance in text classification applications. As the typical semistructured data; the label of XML data possesses the function of definition itself. Label and context of the node can be seen as the text data of this node. ELM offers significant advantages such as fast learning speed, ease of implementation, and effective node classification. Set intersection can compute SLCA quickly in the node sets which is classified by using ELM. In this paper, we adopt ELM to classify nodes and compute probability. We propose two algorithms that are based on ELM and probability threshold to improve the overall performance. The experimental results verify the benefits of our methods according to various evaluation metrics.

scholarly journals Developing and Implementing a Conceptual Composite Materials Design Database

1993 ◽  
Author(s):  
William J. Rasdorf ◽  
Lisa K. Spainhour
Keyword(s):  
Composite Materials ◽  
Computer Technology ◽  
Data Model ◽  
Data Representation ◽  
Future Research ◽  
Clear Understanding ◽  
Analysis And Design ◽  
Problems And Solutions ◽  
Software Interfaces ◽  
Design Software

Abstract Researchers and materials engineers require a greater understanding of the problems and solutions that emerge when integrating composite materials data with computer technology so that utilitarian composite materials databases can be developed to effectively and efficiently support analysis and design software. Composite materials constitute a representational challenge due to their composition and use. However, this paper suggests that a conceptual composite material data model and application software interfaces must be developed to support the dissemination and use of composite materials data. This paper primarily serves to analyze several of the problems facing developers of composite materials databases, evolving from the complexity of the materials themselves and from the current lack of testing and data representation standards. Without a clear understanding of the scope and nature of these problems, there is no possibility of designing concise yet comprehensive composites data models, yet we feel that such an understanding is presently lacking. In addition, an effort is made to present possible solutions to these difficulties being suggested and/or implemented both by the authors and by other researchers in the field. Such an effort provides a firm foundation upon which future research may be based.

A Literature Review of the Emerging Field of IoT Using RFID and Its Applications in Supply Chain Management

2017 ◽  
pp. 1-27 ◽  
Author(s):  
Suvendu Naskar ◽  
Preetam Basu ◽  
Anup K. Sen
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Radio Frequency Identification ◽  
Object Identification ◽  
Future Research ◽  
Primary Concern ◽  
Chain Management ◽  
Space Management ◽  
Extant Literature ◽  
Chain Processes

The Internet of Things (IoT) envisions an ecosystem where smart and interconnected objects can sense surrounding changes, communicate with each other, process information and take active roles in decision making. Optimizing supply chain performance is a primary concern of manufacturing and logistics organizations. Radio Frequency Identification (RFID) is helping organizations to build automated and interconnected smart environment by object identification and tracking, motivating the first step towards an IoT-enabled world. This chapter attempts to understand extant literature studying applications of RFID in implementing the IoT in supply chain management. We categorize extant literature, firstly, based on research methodology and secondly, based on supply chain processes. We find that presently academic activity is around conceptualizing the usability of RFID in the IoT with limited analytical and empirical evidence. Supply chain processes such as demand planning, procurement, retail shelf space management and product returns are prospective areas for interesting future research.

Web Retrieval of XML Documents

2011 ◽  
pp. 170-199
Author(s):  
Barbara Catania ◽  
Elena Ferrari
Keyword(s):  
Expressive Power ◽  
Data Representation ◽  
Query Languages ◽  
Heterogeneous Data ◽  
Data Sources ◽  
Xml Data ◽  
Web Documents ◽  
Web Retrieval ◽  
Heterogeneous Data Sources ◽  
The Web

Web is characterized by a huge amount of very heterogeneous data sources, that differ both in media support and format representation. In this scenario, there is the need of an integrating approach for querying heterogeneous Web documents. To this purpose, XML can play an important role since it is becoming a standard for data representation and exchange over the Web. Due to its flexibility, XML is currently being used as an interface language over the Web, by which (part of) document sources are represented and exported. Under this assumption, the problem of querying heterogeneous sources can be reduced to the problem of querying XML data sources. In this chapter, we first survey the most relevant query languages for XML data proposed both by the scientific community and by standardization committees, e.g., W3C, mainly focusing on their expressive power. Then, we investigate how typical Information Retrieval concepts, such as ranking, similarity-based search, and profile-based search, can be applied to XML query languages. Commercial products based on the considered approaches are then briefly surveyed. Finally, we conclude the chapter by providing an overview of the most promising research trends in the fields.

A Literature Review of the Emerging Field of IoT Using RFID and Its Applications in Supply Chain Management

2020 ◽  
pp. 1993-2017
Author(s):  
Suvendu Naskar ◽  
Preetam Basu ◽  
Anup K. Sen
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Radio Frequency Identification ◽  
Object Identification ◽  
Future Research ◽  
Primary Concern ◽  
Chain Management ◽  
Space Management ◽  
Extant Literature ◽  
Chain Processes

The Internet of Things (IoT) envisions an ecosystem where smart and interconnected objects can sense surrounding changes, communicate with each other, process information and take active roles in decision making. Optimizing supply chain performance is a primary concern of manufacturing and logistics organizations. Radio Frequency Identification (RFID) is helping organizations to build automated and interconnected smart environment by object identification and tracking, motivating the first step towards an IoT-enabled world. This chapter attempts to understand extant literature studying applications of RFID in implementing the IoT in supply chain management. We categorize extant literature, firstly, based on research methodology and secondly, based on supply chain processes. We find that presently academic activity is around conceptualizing the usability of RFID in the IoT with limited analytical and empirical evidence. Supply chain processes such as demand planning, procurement, retail shelf space management and product returns are prospective areas for interesting future research.

scholarly journals Information Fusion for Multi-Source Material Data: Progress and Challenges

2019 ◽  
Vol 9 (17) ◽  
pp. 3473 ◽  
Author(s):  
Zhou ◽  
Hong ◽  
Jin
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Data Fusion ◽  
Manufacturing Industry ◽  
Data Representation ◽  
Source Material ◽  
Future Research ◽  
Unified Framework ◽  
Open Problems ◽  
Material Data

The development of material science in the manufacturing industry has resulted in a huge amount of material data, which are often from different sources and vary in data format and semantics. The integration and fusion of material data can offer a unified framework for material data representation, processing, storage and mining, which can further help to accomplish many tasks, including material data disambiguation, material feature extraction, material-manufacturing parameters setting, and material knowledge extraction. On the other side, the rapid advance of information technologies like artificial intelligence and big data, brings new opportunities for material data fusion. To the best of our knowledge, the community is currently lacking a comprehensive review of the state-of-the-art techniques on material data fusion. This review first analyzes the special properties of material data and discusses the motivations of multi-source material data fusion. Then, we particularly focus on the recent achievements of multi-source material data fusion. This review has a few unique features compared to previous studies. First, we present a systematic categorization and comparison framework for material data fusion according to the processing flow of material data. Second, we discuss the applications and impact of recent hot technologies in material data fusion, including artificial intelligence algorithms and big data technologies. Finally, we present some open problems and future research directions for multi-source material data fusion.

scholarly journals Radio Frequency Identification and Sensing Techniques and Their Applications—A Review of the State-of-the-Art

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 4012 ◽  
Author(s):  
Lei Cui ◽  
Zonghua Zhang ◽  
Nan Gao ◽  
Zhaozong Meng ◽  
Zhen Li
Keyword(s):  
Radio Frequency ◽  
Radio Frequency Identification ◽  
Information Transfer ◽  
Object Identification ◽  
The State ◽  
Future Research ◽  
New Paradigm ◽  
Sensing Applications ◽  
Frequency Identification ◽  
Application Fields

Radio Frequency Identification (RFID) sensors, integrating the features of Wireless Information and Power Transfer (WIPT), object identification and energy efficient sensing capabilities, have been considered a new paradigm of sensing and communication for the futuristic information systems. RFID sensor tags featuring contactless sensing, wireless information transfer, wireless powered, light weight, non-line-of-sight transmission, flexible and pasteable are a critical enabling technology for future Internet-of-Things (IoT) applications, such as manufacturing, logistics, healthcare, agriculture and food. They have attracted numerous research efforts due to their innovative potential in the various application fields. However, there has been a gap between the in-lab investigations and the practical IoT application scenarios, which has motivated this survey of this research to identify the promising enabling techniques and the underlying challenges. This study aims to provide an exhaustive review on the state-of-art RFID sensor technologies from the system implementation perspective by focusing on the fundamental RF energy harvesting theories, the recent technical progresses and commercial solutions, innovative applications and some RFID sensor based IoT solutions, identify the underlying technological challenges at the time being, and give the future research trends and promising application fields in the rich sensing applications of the forthcoming IoT era.

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