A hybrid distributed mutual exclusion algorithm

This paper presents a hybrid approach to distributed mutual exclusion in which two algorithms are combined such that one minimizes message traffic and the other minimizes time delay. In a hybrid approach, sites are divided into groups, and two different algorithms are used to resolve local (intra-group) and global (inter-group) conflicts. In this paper, we develop a hybrid distributed mutual exclusion algorithm which uses Singhal's dynamic information structure algorithm [15] as the local algorithm to minimize time delay and Maekawa's algorithm [7] as the global algorithm to minimize message traffic. Compared to Maekawa's algorithm which needs O(√N) messages, but two time units delay between successive executions of the Critical Section (CS) (where N is the number of sites in the system), the proposed hybrid algorithm can reduce message traffic by 52% and time delay by 29% at the same time.     

An efficient heuristic algorithm for mapping parallel programs onto multicomputers

Mapping, a crucial problem to be solved for optimal utilization of multicomputers, is the problem of allocating the tasks of a parallel program to the processors of a multicomputer in a way that minimizes the total completion time. Unfortunately, this problem is known to be NP-hard and hence we cannot optimally solve it in a reasonable amount of computation time. In this paper, we present a new heuristic algorithm for solving the mapping problem. Our heuristic move-exchange (HME) algorithm works by iterating a sequence of task moves followed by a task exchange in an efficient way and tries to minimize the completion time by minimizing a cost function. This function is a weighted sum of the cost of load imbalance and the cost of all communications. We then present two competing algorithms, viz. the Simulated Annealing (SA) algorithm and the Recursive Mincut (RM) bipartitioning algorithm, and compare the performance of the HME algorithm with that of SA and RM algorithms. The results of our study clearly demonstrate that our HME algorithm is better than both SA and RM algorithms for solving the mapping problem.     

Cost-optimal dynamic data replication for distributed database systems: DYVO

In this paper, we present a dynamic replication algorithm, DYVO, which could provide an optimal cost for operating replicas according to a read/write pattern of transactions. Unlike previous algorithms for dynamic replication, in which read one and write all available approach is used for preserving consistency of replicas, DYVO basically uses quorum approach for managing replicas. The quorum approach is basically able to encompass a large class of failures. Moreover, the quorum approach simplifies considerably the task maintaining the consistency of replicated data. As a result, DYVO is fault-tolerant to all kinds of failures and gives a way that minimizes overload for managing a replication algorithm.     

Graph-based data mining: A new tool for the analysis and comparison of scientific domains represented as scientograms

The creation of some kind of representations depicting the current state of Science (or scientograms) is an established and beaten track for many years now. However, if we are concerned with the automatic comparison, analysis and understanding of a set of scientograms, showing for instance the evolution of a scientific domain or a face-to-face comparison of several countries, the task is titanically complex as the amount of data to analyze becomes huge and complex. In this paper, we aim to show that graph-based data mining tools are useful to deal with scientogram analysis. Subdue, the first algorithm proposed in the graph mining area, has been chosen for this purpose. This algorithm has been customized to deal with three different scientogram analysis tasks regarding the evolution of a scientific domain over time, the extraction of the common research categories substructures in the world, and the comparison of scientific domains between different countries. The outcomes obtained in the developed experiments have clearly demonstrated the potential of graph mining tools in scientogram analysis.     

Bit-permute multistage interconnection networks

In this paper, we consider a class of log N stage interconnection networks called Bit-Permute Multistage Interconnection Networks (BPMIN's) where the ports of each switch of a stage are different at only one bit position of their labels. We describe the decomposition structure of the BPMIN's and prove that all of the BPMIN's are topologically equivalent and some of them are functionally equivalent. We also identify a class of 2 log N stage rearrangeable networks called symmetric BPMIN's where two log N stage BPMIN's are connected in sequence. The symmetric BPMIN's are either symmetric or asymmetric and regular or irregular in their inter-stage connections and can be reduced into 2 log N-1 stages by combining the two center stages. We show that the symmetric BPMIN's constitute larger class of rearrangeable networks than ever known. We also propose a general routing algorithm for the symmetric BPMIN's by modifying slightly the looping algorithm of the Benes network.     

Waves: a fast multi-tier top-<Emphasis Type="Italic">k</Emphasis> query processing algorithm

In this paper, we present Waves, a novel document-at-a-time algorithm for fast computing of top-k query results in search systems. The Waves algorithm uses multi-tier indexes for processing queries. It performs successive tentative evaluations of results which we call waves. Each wave traverses the index, starting from a specific tier level i. Each wave i may insert only those documents that occur in that tier level into the answer. After processing a wave, the algorithm checks whether the answer achieved might be changed by successive waves or not. A new wave is started only if it has a chance of changing the top-k scores. We show through experiments that such lazy query processing strategy results in smaller query processing times when compared to previous approaches proposed in the literature. We present experiments to compare Waves’ performance to the state-of-the-art document-at-a-time query processing methods that preserve top-k results and show scenarios where the method can be a good alternative algorithm for computing top-k results.     

Biblioranking fundamental physics

Counting of number of papers, of citations and the h-index are the simplest bibliometric indices of the impact of research. We discuss some improvements. First, we replace citations with individual citations, fractionally shared among co-authors, to take into account that different papers and different fields have largely different average number of co-authors and of references. Next, we improve on citation counting applying the PageRank algorithm to citations among papers. Being time-ordered, this reduces to a weighted counting of citation descendants that we call PaperRank. We compute a related AuthorRank applying the PageRank algorithm to citations among authors. These metrics quantify the impact of an author or paper taking into account the impact of those authors that cite it. Finally, we show how self- and circular-citations can be eliminated by defining a closed market of Citation-coins. We apply these metrics to the InSpire database that covers fundamental physics, presenting results for papers, authors, journals, institutes, towns, countries for all-time and in recent time periods.     

Linear feature extraction for ranking

We address the feature extraction problem for document ranking in information retrieval. We then propose LifeRank, a Linear feature extraction algorithm for Ranking. In LifeRank, we regard each document collection for ranking as a matrix, referred to as the original matrix. We try to optimize a transformation matrix, so that a new matrix (dataset) can be generated as the product of the original matrix and a transformation matrix. The transformation matrix projects high-dimensional document vectors into lower dimensions. Theoretically, there could be very large transformation matrices, each leading to a new generated matrix. In LifeRank, we produce a transformation matrix so that the generated new matrix can match the learning to rank problem. Extensive experiments on benchmark datasets show the performance gains of LifeRank in comparison with state-of-the-art feature selection algorithms.     

The move to open: medical library leadership in scholarly communication

Over the years, health sciences librarians have been change agents, leading the charge on issues of importance to the profession and the communities we serve. From its founding in 1898 with the Exchange, the Medical Library Association (MLA) has been dedicated to improving access to health information. In 2003, the Board of Directors published a statement supporting open access to information generated from federally funded scientific and medical research and maintained that having access to timely, relevant, and accurate information is vital to the health of the nation and its education and research programs. At some financial risk, the association made the Journal of the Medical Library Association (JMLA) open access and published the entire archive of JMLA and its predecessor, the Bulletin of the Medical Library Association, in PubMed Central. Nearly two decades later, the promise of open access and open science finally seems to be coming to fruition. In the 2020 Janet Doe Lecture, Chris Shaffer, AHIP, described the ways that MLA has led the profession, standing behind a shared vision and “walking the walk.” In challenging listeners to embrace open science, he affirmed that, as leaders in improving access to health sciences information since 1898, medical librarians must work in the open science arena to realize our vision “that quality information is essential for improved health.”

Open in a separate windowChris Shaffer     

Showing results? An analysis of the perceptions of internal and external stakeholders of the public performance communication by the Belgian and Dutch Railways

Performance measurement and communicating about it with the broader public is not self-evident if one looks at public services organizations (Hernon, P., 1998. The government performance and results act. Government Information Quarterly, 15: 153–156). In Belgium and the Netherlands, one organization that has been under constant surveillance from its stakeholders is the railroad company. Originally a national public service, it has changed through a European directive to operate in a liberalized transportation market. In this paper, we present the results of a qualitative study into what the Belgian and Dutch railway companies measure about their performance towards travelers, how they measure it, and specifically how they communicate it to their employees and stakeholders. Although we might expect the Dutch railway company (NS) to be more transparent than the Belgian railway company (NMBS/SNCB), in reality the two organizations do not differ that much from each other.     

Using only cross-document relationships for both generic and topic-focused multi-document summarizations

In recent years graph-ranking based algorithms have been proposed for single document summarization and generic multi-document summarization. The algorithms make use of the “votings” or “recommendations” between sentences to evaluate the importance of the sentences in the documents. This study aims to differentiate the cross-document and within-document relationships between sentences for generic multi-document summarization and adapt the graph-ranking based algorithm for topic-focused summarization. The contributions of this study are two-fold: (1) For generic multi-document summarization, we apply the graph-based ranking algorithm based on each kind of sentence relationship and explore their relative importance for summarization performance. (2) For topic-focused multi-document summarization, we propose to integrate the relevance of the sentences to the specified topic into the graph-ranking based method. Each individual kind of sentence relationship is also differentiated and investigated in the algorithm. Experimental results on DUC 2002–DUC 2005 data demonstrate the great importance of the cross-document relationships between sentences for both generic and topic-focused multi-document summarizations. Even the approach based only on the cross-document relationships can perform better than or at least as well as the approaches based on both kinds of relationships between sentences.

Evaluating university performance using reference point based composite indicators

In this paper, we propose the application of a novel methodology to build composite indicators, in order to evaluate university performance. We analyse separately the three basic dimensions of our university system (research, teaching and technology transfer), because we are interested in getting a more accurate vision of each of them. In order to build the composite indicators, we use a multi-criteria analysis technique, based on the double reference point method. One advantage of this technique is the possibility to use reference levels, in such a way that the results obtained are easily interpreted in terms of the performance of the university with respect to these levels. Besides, aggregations for different compensation degrees are provided. In order to illustrate the advantages of this method, it has been applied to evaluate the performance of the public universities of the Spanish region of Andalucía, for year 2008. The results show that the performance of the Andalusian public universities in the teaching block is better than in the research and technology transfer blocks. The application lets us conclude that the methodology offers a warning system to assist in strategic decision making, and the values of the indicators allow us to find fields of improvement in all areas.     

Characteristic scores and scales based on h-type indices

Based on the rank-order citation distribution of e.g. a researcher, one can define certain points on this distribution, hereby summarizing the citation performance of this researcher. Previous work of Glänzel and Schubert defined these so-called “characteristic scores and scales” (CSS), based on average citation data of samples of this ranked publication–citation list.In this paper we will define another version of CSS, based on diverse h-type indices such as the h-index, the g-index, the Kosmulski's h⁽²⁾-index and the g-variant of it, the g⁽²⁾-index.Mathematical properties of these new CSS are proved in a Lotkaian framework. These CSS also provide an improvement of the single h-type indices in the sense that they give h-type index values for different parts of the ranked publication–citation list.     

Researcher influence prediction (ResIP) using academic genealogy network

In academia researchers join a research community over time and contribute to the advancement of a field in a variety of ways. One of the most established ways to contribute to the field is by passing on knowledge to the future generations through academic advising. Many academic scholars have more influence, while others fail to make an impact. Typically, academic influence refers to the ability of a researcher to pass on her/his “academic gene” in future researchers. In this article, we propose the task of Researcher Influence Prediction (ResIP) to predict researchers’ future influence in an academic field through the analysis of the corresponding academic genealogy network. Researcher influence prediction has got several implications as far as different academic outcomes are concerned (e.g. funding, awards, career progression, collaboration, identifying prolific researchers etc.).To address the ResIP, a number of end-to-end deep learning architectures have been proposed in the current work. The proposed architectures take as input the lineage graph of a researcher at a given time point and predicts the growth of his/her family in future time points. The design of encoder in the proposed architecture considers both temporal and structural information of the input lineage graph while the decoders are tuned towards the nature of the output (single point vs. sequence). The proposed models have been trained, validated and compared with strong baselines using datasets created out of a subset of researchers from the Mathematics Genealogy Project (MGP).     

#nowplaying Madonna: a large-scale evaluation on estimating similarities between music artists and between movies from microblogs

Different term weighting techniques such as $TF\cdot IDF$ or BM25 have been used intensely for manifold text-based information retrieval tasks. Their use for modeling term profiles for named entities and subsequent calculation of similarities between these named entities have been studied to a much smaller extent. The recent trend of microblogging made available massive amounts of information about almost every topic around the world. Therefore, microblogs represent a valuable source for text-based named entity modeling. In this paper, we present a systematic and comprehensive evaluation of different term weighting measures, normalization techniques, query schemes, index term sets, and similarity functions for the task of inferring similarities between named entities, based on data extracted from microblog posts. We analyze several thousand combinations of choices for the above mentioned dimensions, which influence the similarity calculation process, and we investigate in which way they impact the quality of the similarity estimates. Evaluation is performed using three real-world data sets: two collections of microblogs related to music artists and one related to movies. For the music collections, we present results of genre classification experiments using as benchmark genre information from allmusic.com . For the movie collection, we present results of multi-class classification experiments using as benchmark categories from IMDb . We show that microblogs can indeed be exploited to model named entity similarity with remarkable accuracy, provided the correct settings for the analyzed aspects are used. We further compare the results to those obtained when using Web pages as data source.     

Referenced Publication Years Spectroscopy applied to iMetrics: Scientometrics,Journal of Informetrics,and a relevant subset of JASIST

We have developed a (freeware) routine for “Referenced Publication Years Spectroscopy” (RPYS) and apply this method to the historiography of “iMetrics,” that is, the junction of the journals Scientometrics, Informetrics, and the relevant subset of JASIST (approx. 20%) that shapes the intellectual space for the development of information metrics (bibliometrics, scientometrics, informetrics, and webometrics). The application to information metrics (our own field of research) provides us with the opportunity to validate this methodology, and to add a reflection about using citations for the historical reconstruction. The results show that the field is rooted in individual contributions of the 1920s to 1950s (e.g., Alfred J. Lotka), and was then shaped intellectually in the early 1960s by a confluence of the history of science (Derek de Solla Price), documentation (e.g., Michael M. Kessler's “bibliographic coupling”), and “citation indexing” (Eugene Garfield). Institutional development at the interfaces between science studies and information science has been reinforced by the new journal Informetrics since 2007. In a concluding reflection, we return to the question of how the historiography of science using algorithmic means—in terms of citation practices—can be different from an intellectual history of the field based, for example, on reading source materials.     

Object-oriented decomposition for distributed systems

Conventional object-oriented design methodologies lead to a hierarchy of classes, but do not suggest which classes/objects should be loaded on which processors in a distributed system. We present the Decomposition Cost Evaluation Model (DCEM) as an approach to this problem. DCEM brings the mapping problem to a higher level of abstraction where the question is which classes, rather than which tasks, should be loaded on which processors. To support these decisions we define communication and computation cost functions for objects, classes, and hierarchies.We then introduce Confined Space Search Decomposition (CSSD) which enhances parallel operations of applications utilizing a tree topology for the processor interconnection scheme. To reduce the penalties of load imbalance, we include a distributed dynamic load balancing heuristic called Object Reincarnation (OR) in which no additional communication costs are incurred.     

Increasing evaluation sensitivity to diversity

Many queries have multiple interpretations; they are ambiguous or underspecified. This is especially true in the context of Web search. To account for this, much recent research has focused on creating systems that produce diverse ranked lists. In order to validate these systems, several new evaluation measures have been created to quantify diversity. Ideally, diversity evaluation measures would distinguish between systems by the amount of diversity in the ranked lists they produce. Unfortunately, diversity is also a function of the collection over which the system is run and a system’s performance at ad-hoc retrieval. A ranked list built from a collection that does not cover multiple subtopics cannot be diversified; neither can a ranked list that contains no relevant documents. To ensure that we are assessing systems by their diversity, we develop (1) a family of evaluation measures that take into account the diversity of the collection and (2) a meta-evaluation measure that explicitly controls for performance. We demonstrate experimentally that our new measures can achieve substantial improvements in sensitivity to diversity without reducing discriminative power.     

Socializing the h-index

A variety of bibliometric measures have been proposed to quantify the impact of researchers and their work. The h-index is a notable and widely used example which aims to improve over simple metrics such as raw counts of papers or citations. However, a limitation of this measure is that it considers authors in isolation and does not account for contributions through a collaborative team. To address this, we propose a natural variant that we dub the Social h-index. The idea is to redistribute the h-index score to reflect an individual's impact on the research community. In addition to describing this new measure, we provide examples, discuss its properties, and contrast with other measures.