Ringo

Abstract

We present Ringo, a system for analysis of large graphs. Graphs provide a way to represent and analyze systems of interacting objects (people, proteins, webpages) with edges between the objects denoting interactions (friendships, physical interactions, links). Mining graphs provides valuable insights about individual objects as well as the relationships among them. In building Ringo, we take advantage of the fact that machines with large memory and many cores are widely available and also relatively affordable. This allows us to build an easy-to-use interactive high-performance graph analytics system. Graphs also need to be built from input data, which often resides in the form of relational tables. Thus, Ringo provides rich functionality for manipulating raw input data tables into various kinds of graphs. Furthermore, Ringo also provides over 200 graph analytics functions that can then be applied to constructed graphs. We show that a single big-memory machine provides a very attractive platform for performing analytics on all but the largest graphs as it offers excellent performance and ease of use as compared to alternative approaches. With Ringo, we also demonstrate how to integrate graph analytics with an iterative process of trial-and-error data exploration and rapid experimentation, common in data mining workloads.

Holm Oak Somatic Embryogenesis: Current Status and Future Perspectives

“…In the last years, knowledge about gene regulation of plant differentiation has speed out and genes involved on the induction of embryogenic competence of somatic cells have been identified in woody plant species, mainly conifers (Rupps et al., 2016; Gautier et al., 2018). But, in oaks and in spite of its ecological and economical importance, very few reports have been released concerning genetic control of SE induction (Šunderlíková et al., 2009; Valladares et al., 2013) and maturation (Šunderlíková and Wilhem, 2002; Pérez et al., 2015). More recently, DNA demethylation has been associated with SE initiation in Q. alba (Corredoira et al., 2017b).…”

Adaptive Low-level Storage of Very Large Knowledge Graphs

“…come at the price of higher communication cost and increased system complexity [71]. Moreover, sometimes distributed solutions cannot be used either due to financial or privacy-related constraints.…”

“…Centralized architectures, in contrast, do not have network costs, are commonly affordable, and provide enough resources to load all-but-the-largest graphs. Some centralized storage engines have demonstrated that they can handle large graphs, but they focus primarily on supporting one particular type of workload (e.g., Ringo [71] supports graph analytics, RDF engines like Virtuoso [67] or RDFox [60] focus on SPARQL [39]). To the best of our knowledge, we still lack a single storage solution that can handle very large KGs as well as support multiple workloads.…”

Building Chinese interdisciplinary research centers : the case of Tsinghua University

“…First, the typical memory capacities of the fast and slow memories keep diverging, resulting in increasingly higher slow-to-fast capacity ratios. Memory-intensive big-data applications like neural networks [38], [44], [46], [47], genome alignment [18], [19], [39], and graph analytics [22], [58], [61], [71], [73] have been continuously driving the growth of main memory capacity. The emerging NVM technologies, such as phase-change memories [78] and Intel 3D XPoint [29], promise to handily extend the byte-addressable memory in a system to TB scales.…”