This is a publisher ranking study based on a citation data grant from Elsevier, specifically, book titles cited in Scopus history journals (2007–2011) and matching metadata from WorldCat® (i.e., OCLC numbers, ISBN codes, publisher records, and library holding counts). Using both resources, we have created a unique relational database designed to compare citation counts to books with international library holdings or libcitations for scholarly book publishers. First, we construct a ranking of the top 500 publishers and explore descriptive statistics at the level of publisher type (university, commercial, other) and country of origin. We then identify the top 50 university presses and commercial houses based on total citations and mean citations per book (CPB). In a third analysis, we present a map of directed citation links between journals and book publishers. American and British presses/publishing houses tend to dominate the work of library collection managers and citing scholars; however, a number of specialist publishers from Europe are included. Distinct clusters from the directed citation map indicate a certain degree of regionalism and subject specialization, where some journals produced in languages other than English tend to cite books published by the same parent press. Bibliometric rankings convey only a small part of how the actual structure of the publishing field has evolved; hence, challenges lie ahead for developers of new citation indices for books and bibliometricians interested in measuring book and publisher impacts.

The Matrix Framework is a recent proposal by Information Retrieval (IR) researchers to flexibly represent information retrieval models and concepts in a single multidimensional array framework. We provide computational support for exactly this framework with the array database system SRAM (Sparse Relational Array Mapping), that works on top of a DBMS. Information retrieval models can be specified in its comprehension-based array query language, in a way that directly corresponds to the underlying mathematical formulas. SRAM efficiently stores sparse arrays in (compressed) relational tables and translates and optimizes array queries into relational queries. In this work, we describe a number of array query optimization rules. To demonstrate their effect on text retrieval, we apply them in the TREC TeraByte track (TREC-TB) efficiency task, using the Okapi BM25 model as our example. It turns out that these optimization rules enable SRAM to automatically translate the BM25 array queries into the relational equivalent of inverted list processing including compression, score materialization and quantization, such as employed by custom-built IR systems. The use of the high-performance MonetDB/X100 relational backend, that provides transparent database compression, allows the system to achieve very fast response times with good precision and low resource usage.

Abstract. Non-trivial retrieval applications involve complex computations on large multi-dimensional datasets. These should, in principle, benefit from the use of relational database technology. However, expressing such problems in terms of relational queries is difficult and timeconsuming. Even more discouraging is the efficiency issue: query optimization strategies successful in classical relational domains may not suffice when applied to the multi-dimensional array domain. The RAM (Relational Array Mapping) system hides these difficulties by providing a transparent mapping between the scientific problem specification and the underlying database system. In addition, its optimizer is specifically tuned to exploit the characteristics of the array paradigm and to allow for automatic balanced work-load distribution. Using an example taken from the multimedia domain, this paper shows how a distributed realword application can be efficiently implemented, using the RAM system, without user intervention.

This paper details a unique data experiment carried out at the University of Amsterdam, Center for Digital Humanities. Data pertaining to monographs were collected from three autonomous resources, the Scopus Journal Index, WorldCat.org and Goodreads, and linked according to unique identifiers in a new Microsoft SQL database. The purpose of the experiment was to investigate co-varied metrics for a list of book titles based on their citation impact (from Scopus), presence in international libraries (WorldCat.org) and visibility as publically reviewed items (Goodreads). The results of our data experiment highlighted current problems related citation indices and the way that books are recorded by different citing authors. Our research further demonstrates the primary problem of matching book titles as 'cited objects' with book titles held in a union library catalog, given that books are always recorded distinctly in libraries if published as separate editions with different International Standard Book Numbers (ISBNs). Due to various 'matching' problems related to the ISBN, we suggest a new type of identifier, a 'Book Object Identifier', which would allow bibliometricians to recognize a book published in multiple formats and editions as 'one object' suitable for evaluation. The BOI standard would be most useful for books published in the same language, and would more easily support the integration of data from different types of book indexes.

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