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Yannis Manolopoulos

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Digital Libraries Databases Information Retrieval physics.soc-ph Social and Information Networks

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preprint2026arXiv

A contemporary science map through the lens of IEEE and ACM periodicals

ACM and IEEE are the two premier associations on computing and electrical/electronics engineering which publish and organize the great majority of periodicals and conferences, respectively, serving these disciplines. Science is a constantly evolving process, and these publication fora are expected to follow the trends. In this article, we focus on the periodicals published by the two associations and seek to detect and/or confirm any contemporary science trends as these are reflected to the periodical titles established recently. Our study is rather qualitative than quantitative, aiming at revealing patterns immediately comprehensible and validatable by the reader. Among the most notable patterns, we see a growing preference of both associations for the open access mode of publication; we also observe ACM's orientation toward AI-focused periodicals, and most importantly, a significant theme overlap among periodicals of the same association and this is valid for both ACM and IEEE.

preprint2014arXiv

Identification of Influential Scientists vs. Mass Producers by the Perfectionism Index

The concept of h-index has been proposed to easily assess a researcher's performance with a single number. However, by using only this number, we lose significant information about the distribution of citations per article in an author's publication list. In this article, we study an author's citation curve and we define two new areas related to this curve. We call these "penalty areas", since the greater they are, the more an author's performance is penalized. We exploit these areas to establish new indices, namely PI and XPI, aiming at categorizing researchers in two distinct categories: "influentials" and "mass producers"; the former category produces articles which are (almost all) with high impact, and the latter category produces a lot of articles with moderate or no impact at all. Using data from Microsoft Academic Service, we evaluate the merits mainly of PI as a useful tool for scientometric studies. We establish its effectiveness into separating the scientists into influentials and mass producers; we demonstrate its robustness against self-citations, and its uncorrelation to traditional indices. Finally, we apply PI to rank prominent scientists in the areas of databases, networks and multimedia, exhibiting the strength of the index in fulfilling its design goal.

preprint2013arXiv

Categorizing Influential Authors Using Penalty Areas

The concept of h-index has been proposed to easily assess a researcher's performance with a single two-dimensional number. However, by using only this single number, we lose significant information about the distribution of the number of citations per article of an author's publication list. Two authors with the same h-index may have totally different distributions of the number of citations per article. One may have a very long "tail" in the citation curve, i.e. he may have published a great number of articles, which did not receive relatively many citations. Another researcher may have a short tail, i.e. almost all his publications got a relatively large number of citations. In this article, we study an author's citation curve and we define some areas appearing in this curve. These areas are used to further evaluate authors' research performance from quantitative and qualitative point of view. We call these areas as "penalty" ones, since the greater they are, the more an author's performance is penalized. Moreover, we use these areas to establish new metrics aiming at categorizing researchers in two distinct categories: "influential" ones vs. "mass producers".

preprint2012arXiv

ART : Sub-Logarithmic Decentralized Range Query Processing with Probabilistic Guarantees

We focus on range query processing on large-scale, typically distributed infrastructures, such as clouds of thousands of nodes of shared-datacenters, of p2p distributed overlays, etc. In such distributed environments, efficient range query processing is the key for managing the distributed data sets per se, and for monitoring the infrastructure's resources. We wish to develop an architecture that can support range queries in such large-scale decentralized environments and can scale in terms of the number of nodes as well as in terms of the data items stored. Of course, in the last few years there have been a number of solutions (mostly from researchers in the p2p domain) for designing such large-scale systems. However, these are inadequate for our purposes, since at the envisaged scales the classic logarithmic complexity (for point queries) is still too expensive while for range queries it is even more disappointing. In this paper we go one step further and achieve a sub-logarithmic complexity. We contribute the ART, which outperforms the most popular decentralized structures, including Chord (and some of its successors), BATON (and its successor) and Skip-Graphs. We contribute theoretical analysis, backed up by detailed experimental results, showing that the communication cost of query and update operations is $O(\log_{b}^2 \log N)$ hops, where the base $b$ is a double-exponentially power of two and $N$ is the total number of nodes. Moreover, ART is a fully dynamic and fault-tolerant structure, which supports the join/leave node operations in $O(\log \log N)$ expected w.h.p number of hops. Our experimental performance studies include a detailed performance comparison which showcases the improved performance, scalability, and robustness of ART.