Distributed Community Detection with the WCC Metric

Community detection has become an extremely active area of research in recent years, with researchers proposing various new metrics and algorithms to address the problem. Recently, the Weighted Community Clustering (WCC) metric was proposed as a novel way to judge the quality of a community partitioning based on the distribution of triangles in the graph, and was demonstrated to yield superior results over other commonly used metrics like modularity. The same authors later presented a parallel algorithm for optimizing WCC on large graphs. In this paper, we propose a new distributed, vertex-centric algorithm for community detection using the WCC metric. Results are presented that demonstrate the algorithm's performance and scalability on up to 32 worker machines and real graphs of up to 1.8 billion vertices. The algorithm scales best with the largest graphs, and to our knowledge, it is the first distributed algorithm for optimizing the WCC metric.Comment: 6 pages, 6 figure

A Multi-layer Collaborative Cache for Question Answering ⋆

Abstract. This paper is the first analysis of caching architectures for Question Answering (QA). We introduce the novel concept of multi-layer collaborative caches, where: (a) each resource intensive QA component is allocated a distinct segment of the cache, and (b) the overall cache is transparently spread across all nodes of the distributed system. We empirically analyze the proposed architecture using a real-world QA system installed on a cluster of 16 nodes. Our analysis indicates that multi-layer collaborative caches induce an almost two fold reduction in QA execution time compared to a QA system with local cache.

ParallelGDB: A Parallel Graph Database Based on Cache Specialization

International audienceThe need for managing massive attributed graphs is becoming common in many areas such as recommendation systems, proteomics analysis, social network analysis or bibliographic analysis. This is making it necessary to move towards parallel systems that allow managing graph databases containing millions of vertices and edges. Previous work on distributed graph databases has focused on finding ways to partition the graph to reduce network traffic and improve execution time. However, partitioning a graph and keeping the information regarding the location of vertices might be unrealistic for massive graphs. In this paper, we propose Parallel-GDB, a new system based on specializing the local caches of any node in this system, providing a better cache hit ratio. ParallelGDB uses a random graph partitioning, avoiding complex partition methods based on the graph topology, that usually require managing extra data structures. This proposed system provides an efficient environment for distributed graph databases

Two-way Replacement Selection

The performance of external sorting using merge sort is highly dependent on the length of the runs generated. One of the most commonly used run generation strategies is Replacement Selection (RS) because, on average, it generates runs that are twice the size of the memory available. However, the length of the runs generated by RS is downsized for data with certain characteristics, like inputs sorted inversely with respect to the desired output order. The goal of this paper is to propose and analyze two-way replacement selection (2WRS), which is a generalization of RS obtained by implementing two heaps instead of the single heap implemented by RS. The appropriate management of these two heaps allows generating runs larger than the memory available in a stable way, i.e. independent from the characteristics of the datasets. Depending on the changing characteristics of the input dataset, 2WRS assigns a new data record to one or the other heap, and grows or shrinks each heap, accommodating to the growing or decreasing tendency of the dataset. On average, 2WRS creates runs of at least the length generated by RS, and longer for datasets that combine increasing and decreasing data subsets. We tested both algorithms on large datasets with different characteristics and 2WRS achieves speedups at least similar to RS, and over 2.5 when RS fails to generate large runs

1 Using Evolutive Summary Counters for Efficient Cooperative Caching in Search Engines

Abstract—We propose and analyze a distributed cooperative caching strategy based on the Evolutive Summary Counters (ESC), a new data structure that stores an approximated record of the data accesses in each computing node of a search engine. The ESC capture the frequency of accesses to the elements of a data collection, and the evolution of the access patterns for each node in a network of computers. The ESC can be efficiently summarized into what we call ESC-summaries to obtain approximate statistics of the document entries accessed by each computing node. We use the ESC-summaries to introduce two algorithms that manage our distributed caching strategy, one for the distribution of the cache contents, ESC-placement, and another one for the search of documents in the distributed cache, ESC-search. While the former improves the hit rate of the system and keeps a large ratio of data accesses local, the latter reduces the network traffic by restricting the number of nodes queried to find a document. We show that our cooperative caching approach outperforms state of the art models in both hit rate, throughput, and location recall for multiple scenarios, i.e., different query distributions and systems with varying degrees of complexity