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Internet + and Electronic Business in China is a comprehensive resource that provides insight and analysis into E-commerce in China and how it has revolutionized and continues to revolutionize business and society. Split into four distinct sections, the book first lays out the theoretical foundations and fundamental concepts of E-Business before moving on to look at internet+ innovation models and their applications in different industries such as agriculture, finance and commerce. The book then provides a comprehensive analysis of E-business platforms and their applications in China before finishing with four comprehensive case studies of major E-business projects, providing readers with successful examples of implementing E-Business entrepreneurship projects.

Internet + and Electronic Business in China is a comprehensive resource that provides insights and analysis into how E-commerce has revolutionized and continues to revolutionize business and society in China.

This paper will discuss the integration of document image processing and text retrieval principles in order to process and load existing paper documents automatically in an electronic document database that broadens the user's capability to retrieve relevant information more accurately, without going through costly processes to get paper documents into electronic text. The principles of document image processing systems, as well as the problems and shortcomings of most of today's document image processing systems, will be discussed. Then concept retrieval as the latest development in text retrieval will be discussed, with specific reference to the ability of the TOPIC intelligent text retrieval system to allow users to build up a knowledge base of search objects or concepts that can be used at any point in time by all users for the system. This paper will further specifically look at the automatic processing of paper documents by converting the scanned document image pages through to electronic text. The use of optical character recognition technology, the indexing and loading of the documents in a text database, the automatic linking of the documents to the related document images and the retrieval technology available in TOPIC, specifically the TYPO operator that was developed to handle so‐called dirty data such as the common misspellings, character transpositions and ‘dirty’ text received as output from the OCR process, will be discussed. A possible solution to load paper documents quickly and cost‐effectively into an electronic document database will be discussed and demonstrated in detail. The advantages and disadvantages of this approach will be discussed with specific reference to an electronic news clipping service application.

State-of-the-art cloud applications are problematic for collaborative document management; their current design does not encourage active personal folder categorization. Cloud applications such as Google Drive and Microsoft’s OneDrive store documents automatically, so at no point are users directed to categorize them by placing them in folders. To encourage active categorization and promote effective retrieval of cloud documents, the authors designed an add-on “nudge” called Personal Organizer which prompts Google Drive users to categorize by storing cloud documents in personal folders. The add-on prompt is triggered when users attempt to close uncategorized or unnamed documents. The purpose of this paper is to test whether using the Personal Organizer add-on leads participants to actively store their documents in folders that they personally created, and whether this promotes more successful and efficient retrieval.

To test the add-on, the authors conducted a pretest-manipulation-post-test intervention study with 34 participants lasting over three months. In both tests, participants were asked to retrieve personal documents taken from their own “Recents” list to improve ecological validity.

Using our add-on doubled the percentage of documents that were actively stored in folders. Additionally, using personally created folders substantially improved retrieval success while decreasing retrieval time.

Implementing our findings can improve document storage and retrieval for millions of users of collaborative cloud storage. The authors discuss broader theoretical implications concerning the role of active organization for retrieval in collaborative repositories, as well as design implications.

This article reviews the state of the art in automatic indexing, that is, automatic techniques for analysing and characterising documents, for manipulating their descriptions in searching, and for generating the index language used for these purposes. It concentrates on the literature from 1968 to 1973. Section I defines the topic and its context. Sections II and III consider work in syntax and semantics respectively in detail. Section IV comments on ‘indirect’ indexing. Section V briefly surveys operating mechanized systems. In Section VI major experiments in automatic indexing are reviewed, and Section VII attempts an overall conclusion on the current state of automatic indexing techniques.

In this paper methods for both speeding up passage processing and examining more passages using parallel computers are explored. The number of passages processed are varied in order to examine the effect on retrieval effectiveness and efficiency. The particular algorithm applied has previously been used to good effect in Okapi experiments at TREC. This algorithm and the mechanism for applying parallel computing to speed up processing are described.

Present and possible future developments in the techniques of document management are reviewed, the major ones being text retrieval and scanning and OCR. Acquisition, indexing and thesauri, publishing and dissemination and the document management industry are also addressed. The emerging standards are reviewed and the impact of the Internet is analysed.

INSTRUCT is a multi‐user, text retrieval system which was developed as an interactive teaching package for demonstrating modern information retrieval techniques, these including natural language query processing, best match searching and automatic relevance feedback based on probabilistic term weighting. INSTRUCT has recently been extended and now additionally has facilities for query expansion using both relevance and term co‐occurrence data, for cluster‐based searching and for two browsing search strategies. These retrieval mechanisms are used to search a file of 26,280 titles and abstracts from the Library and Information Science Abstracts database; both menu‐based and command‐based searching are allowed.

Full‐text documents are usually searched by means of a Boolean retrieval algorithm that requires the user to specify the logical relationships between the terms of a query. In this paper, we summarise the results to date of a continuing programme of research at the University of Sheffield to investigate the use of nearest‐neighbour retrieval algorithms for full‐text searching. Given a natural‐language query statement, our methods result in a ranking of the paragraphs comprising a full‐text document in order of decreasing similarity with the query, where the similarity for each paragraph is determined by the number of keyword stems that it has in common with the query. A full‐text document test collection has been created to allow systematic tests of retrieval effectiveness to be carried out. Experiments with this collection demonstrate that nearest‐neighbour searching provides a means for paragraph‐based access to full‐text documents that is of comparable effectiveness to both Boolean and hypertext searching and that index term weighting schemes which have been developed for the searching of bibliographical databases can also be used to improve the effectiveness of retrieval from full‐text databases. A current project is investigating the extent to which a paragraph‐based full‐text retrieval system can be used to augment the explication facilities of an expert system on welding.

In existing information retrieval models there are three different ways documents are represented for retrieval purposes: vectors of weights, collections of sentences and artificial neurons. Accordingly, retrieval depends on a similarity function, or means an inference, or is a spreading of activation. Relevancy is considered to be a critical modelling parameter which is either a priori or it is not treated at all. Assuming that relevancy may equally be an emergent entity, thus not requiring any a priori modelling, the paper proposes the Interaction Information Retrieval model in which documents are interconnected, queries and documents are treated in the same way, and in which retrieval is the result of the interconnection between query and documents. Algorithms and experiences gained with practical applications are presented. A theoretical mathematical formulation of this type of retrieval is also given.