Computing Web-scale Topic Models using an Asynchronous Parameter Server

Topic models such as Latent Dirichlet Allocation (LDA) have been widely used in information retrieval for tasks ranging from smoothing and feedback methods to tools for exploratory search and discovery. However, classical methods for inferring topic models do not scale up to the massive size of today's publicly available Web-scale data sets. The state-of-the-art approaches rely on custom strategies, implementations and hardware to facilitate their asynchronous, communication-intensive workloads. We present APS-LDA, which integrates state-of-the-art topic modeling with cluster computing frameworks such as Spark using a novel asynchronous parameter server. Advantages of this integration include convenient usage of existing data processing pipelines and eliminating the need for disk writes as data can be kept in memory from start to finish. Our goal is not to outperform highly customized implementations, but to propose a general high-performance topic modeling framework that can easily be used in today's data processing pipelines. We compare APS-LDA to the existing Spark LDA implementations and show that our system can, on a 480-core cluster, process up to 135 times more data and 10 times more topics without sacrificing model quality.Comment: To appear in SIGIR 201

Parallel Architectures for Planetary Exploration Requirements (PAPER)

The Parallel Architectures for Planetary Exploration Requirements (PAPER) project is essentially research oriented towards technology insertion issues for NASA's unmanned planetary probes. It was initiated to complement and augment the long-term efforts for space exploration with particular reference to NASA/LaRC's (NASA Langley Research Center) research needs for planetary exploration missions of the mid and late 1990s. The requirements for space missions as given in the somewhat dated Advanced Information Processing Systems (AIPS) requirements document are contrasted with the new requirements from JPL/Caltech involving sensor data capture and scene analysis. It is shown that more stringent requirements have arisen as a result of technological advancements. Two possible architectures, the AIPS Proof of Concept (POC) configuration and the MAX Fault-tolerant dataflow multiprocessor, were evaluated. The main observation was that the AIPS design is biased towards fault tolerance and may not be an ideal architecture for planetary and deep space probes due to high cost and complexity. The MAX concepts appears to be a promising candidate, except that more detailed information is required. The feasibility for adding neural computation capability to this architecture needs to be studied. Key impact issues for architectural design of computing systems meant for planetary missions were also identified

Steps in Metagenomics: Let’s Avoid Garbage in and Garbage Out

Is metagenomics a revolution or a new fad? Metagenomics is tightly associated with the availability of next-generation sequencing in all its implementations. The key feature of these new technologies, moving beyond the Sanger-based DNA sequencing approach, is the depth of nucleotide sequencing per sample.1 Knowing much more about a sample changes the traditional paradigms of “What is the most abundant?” or “What is the most significant?” to “What is present and potentially sig­nificant that might influence the situation and outcome?” Let’s take the case of identifying proper biomarkers of disease state in the context of chronic disease prevention. Prevention has been deemed as a viable option to avert human chronic diseases and to curb health­care management costs.2 The actual implementation of any effective preventive measures has proven to be rather difficult. In addition to the typically poor compliance of the general public, the vagueness of the successful validation of habit modification on the long-term risk, points to the need of defining new biomarkers of disease state. Scientists and the public are accepting the fact that humans are super-organisms, harboring both a human genome and a microbial genome, the latter being much bigger in size and diversity, and key for the health of individuals.3,4 It is time to investigate the intricate relationship between humans and their associated microbiota and how this relationship mod­ulates or affects both partners.5 These remarks can be expanded to the animal and plant kingdoms, and holistically to the Earth’s biome. By its nature, the evolution and function of all the Earth’s biomes are influenced by a myriad of interactions between and among microbes (planktonic, in biofilms or host associated) and the surrounding physical environment. The general definition of metagenomics is the cultivation-indepen­dent analysis of the genetic information of the collective genomes of the microbes within a given environment based on its sampling. It focuses on the collection of genetic information through sequencing that can target DNA, RNA, or both. The subsequent analyses can be solely fo­cused on sequence conservation, phylogenetic, phylogenomic, function, or genetic diversity representation including yet-to-be annotated genes. The diversity of hypotheses, questions, and goals to be accomplished is endless. The primary design is based on the nature of the material to be analyzed and its primary function

An efficient closed frequent itemset miner for the MOA stream mining system

Mining itemsets is a central task in data mining, both in the batch and the streaming paradigms. While robust, efficient, and well-tested implementations exist for batch mining, hardly any publicly available equivalent exists for the streaming scenario. The lack of an efficient, usable tool for the task hinders its use by practitioners and makes it difficult to assess new research in the area. To alleviate this situation, we review the algorithms described in the literature, and implement and evaluate the IncMine algorithm by Cheng, Ke, and Ng (2008) for mining frequent closed itemsets from data streams. Our implementation works on top of the MOA (Massive Online Analysis) stream mining framework to ease its use and integration with other stream mining tasks. We provide a PAC-style rigorous analysis of the quality of the output of IncMine as a function of its parameters; this type of analysis is rare in pattern mining algorithms. As a by-product, the analysis shows how one of the user-provided parameters in the original description can be removed entirely while retaining the performance guarantees. Finally, we experimentally confirm both on synthetic and real data the excellent performance of the algorithm, as reported in the original paper, and its ability to handle concept drift.Postprint (published version