Academic careers in Computer Science: Continuance and transience of lifetime co-authorships

International audienceScholarly publications reify fruitful collaborations between co-authors. A branch of research in the Science Studies focuses on analyzing the co-authorship networks of established scientists. Such studies tell us about how their collaborations developed through their careers. This paper updates previous work by reporting a transversal and a longitudinal studies spanning the lifelong careers of a cohort of researchers from the DBLP bibliographic database. We mined 3,860 researchers' publication records to study the evolution patterns of their co-authorships. Two features of co-authors were considered: 1) their expertise, and 2) the history of their partnerships with the sampled researchers. Our findings reveal the ephemeral nature of most collaborations: 70% of the new co-authors were only one-shot partners since they did not appear to collaborate on any further publications. Overall, researchers consistently extended their co-authorships 1) by steadily enrolling beginning researchers (i.e., people who had never published before), and 2) by increasingly working with confirmed researchers with whom they already collaborated

Load distribution fairness in P2P data management systems

We address the issue of measuring storage, or query load distribution fairness in peer-to-peer data management systems. Existing metrics may look promising from the point of view of specific peers, while in reality being far from optimal from a global perspective. Thus, first we define the requirements and study the appropriateness of various statistical metrics for measuring load distribution fairness towards these requirements. The metric proposed as most appropriate is the Gini coefficient (G). Second, we develop novel distributed sampling algorithms to compute G on-line, with high precision, efficiently, and scalably. Third, we show how G can readily be utilized on-line by higher-level algorithms which can now know when to best intervene to correct load imbalances. Our analysis and experiments testify for the efficiency and accuracy of these algorithms, permitting the online use of a rich and reliable metric, conveying a global perspective of the distribution

Towards a unifying framework for complex query processing over structured peer-to-peer data networks

Abstract. In this work we study how to process complex queries in DHT-based Peer-to-Peer (P2P) data networks. Queries are made over tuples and relations and are expressed in a query language, such as SQL. We describe existing research approaches for query processing in P2P systems, we suggest improvements and enhancements, and propose a unifying framework that consists of a modified DHT architecture, data placement and search algorithms, and provides efficient support for processing a variety of query types, including queries with one or more attributes, queries with selection operators (involving equality and range queries), and queries with join operators. To our knowledge, this is the first work that puts forth a framework providing support for all these query types.

Self-join size estimation in large-scale distributed data systems

In this work we tackle the open problem of self-join size (SJS) estimation in a large-scale distributed data system, where tuples of a relation are distributed over data nodes which comprise an overlay network. Our contributions include adaptations of five well-known SJS estimation centralized techniques (coined sequential, cross-sampling, adaptive, bifocal, and sample-count) to the network environment and a novel technique which is based on the use of the Gini coefficient. We develop analyses showing how Gini estimations can lead to estimations of the underlying Zipfian or power-law value distributions. We further contribute distributed sampling algorithms that can estimate accurately and efficiently the Gini coefficient. Finally, we provide detailed experimental evidence testifying for the claimed increased accuracy, precision, and efficiency of the proposed SJS estimation method, compared to the other methods. The proposed approach is the only one to ensure high efficiency, precision, and accuracy regardless of the skew of the underlying data

Saturn: range queries, load balancing and fault tolerance in DHT data systems

In this paper, we present Saturn, an overlay architecture for large-scale data networks maintained over Distributed Hash Tables (DHTs) that efficiently processes range queries and ensures access load balancing and fault-tolerance. Placing consecutive data values in neighboring peers is desirable in DHTs since it accelerates range query processing; however, such a placement is highly susceptible to load imbalances. At the same time, DHTs may be susceptible to node departures/failures and high data availability and fault tolerance are significant issues. Saturn deals effectively with these problems through the introduction of a novel multiple ring, order-preserving architecture. The use of a novel order-preserving hash function ensures fast range query processing. Replication across and within data rings (termed vertical and horizontal replication) forms the foundation over which our mechanisms are developed, ensuring query load balancing and fault tolerance, respectively. Our detailed experimentation study shows strong gains in range query processing efficiency, access load balancing, and fault tolerance, with low replication overheads. The significance of Saturn is not only that it effectively tackles all three issues together—i.e., supporting range queries, ensuring load balancing, and providing fault tolerance over DHTs—but also that it can be applied on top of any order-preserving DHT enabling it to dynamically handle replication and, thus, to trade off replication costs for fair load distribution and fault tolerance