In their very own words: authors and scholarly journal publishing

This paper presents the results of a major investigation by questionnaire of author attitudes towards scholarly publishing, which was funded by the Publisher's Association. Altogether, the views of nearly 4,000 authors were obtained and this paper concerns just those thousand or so authors who accepted the invitation to write freely about their own personal feelings and experiences. Using QSR N6 textual analysis software, their comments were processed and themes extracted. While the rest of the questionnaire, which was formed of closed‐question, tick‐box type questions, clearly must have had some impact on their thinking, the authors had considerable latitude in regard to what they could say. The paper provides a unique opportunity to hear their views directly. The pressing topics turned out to be (in rough order of concern): the peer‐review system, copyright, journal prices, alternative business models for journal publishing, big deals, Elsevier, electronic publishing and digital journals.     

A pipelined architecture for distributed text query evaluation

Two principal query-evaluation methodologies have been described for cluster-based implementation of distributed information retrieval systems: document partitioning and term partitioning. In a document-partitioned system, each of the processors hosts a subset of the documents in the collection, and executes every query against its local sub-collection. In a term-partitioned system, each of the processors hosts a subset of the inverted lists that make up the index of the collection, and serves them to a central machine as they are required for query evaluation. In this paper we introduce a pipelined query-evaluation methodology, based on a term-partitioned index, in which partially evaluated queries are passed amongst the set of processors that host the query terms. This arrangement retains the disk read benefits of term partitioning, but more effectively shares the computational load. We compare the three methodologies experimentally, and show that term distribution is inefficient and scales poorly. The new pipelined approach offers efficient memory utilization and efficient use of disk accesses, but suffers from problems with load balancing between nodes. Until these problems are resolved, document partitioning remains the preferred method. Alistair Moffat was supported by the Australian Research Council, the ARC Special Research Center for Perceptive and Intelligent Machines in Complex Environments, and the NICTA Victoria Laboratory. William Webber and Justin Zobel were supported by the Australian Research Council. Ricardo Baeza-Yates was supported by Grant P01-029-F from Millennium Initiative of Mideplan, Chile; and by the University of Melbourne as a visiting scholar at the time this project was undertaken.     

India: Demographics for Publishers

The essential demographic data and related information for India is provided specifically for the publishing industry. The focus has been placed on the economics, education and computer industry in relation to the complex cultural factors of different languages and religions. The role of India in relation to the other three BRIC countries or Brazil, China and Russia is explored.     

Diversity in the Information Seeking Behaviour of the Virtual Scholar: Institutional Comparisons

The logs of four universities using the OhioLINK journal system were evaluated for a period of fifteen months using deep log analysis methods in order to compare and contrast the information seeking behaviour of their users. Large differences were found, especially between the research and teaching universities. Methodological problems associated with making the comparisons are discussed in some detail especially in terms of defining online sessions.     

Searching strategies for the Bulgarian language

This paper reports on the underlying IR problems encountered when indexing and searching with the Bulgarian language. For this language we propose a general light stemmer and demonstrate that it can be quite effective, producing significantly better MAP (around + 34%) than an approach not applying stemming. We implement the GL2 model derived from the Divergence from Randomness paradigm and find its retrieval effectiveness better than other probabilistic, vector-space and language models. The resulting MAP is found to be about 50% better than the classical tf idf approach. Moreover, increasing the query size enhances the MAP by around 10% (from T to TD). In order to compare the retrieval effectiveness of our suggested stopword list and the light stemmer developed for the Bulgarian language, we conduct a set of experiments on another stopword list and also a more complex and aggressive stemmer. Results tend to indicate that there is no statistically significant difference between these variants and our suggested approach. This paper evaluates other indexing strategies such as 4-gram indexing and indexing based on the automatic decompounding of compound words. Finally, we analyze certain queries to discover why we obtained poor results, when indexing Bulgarian documents using the suggested word-based approach.