Machine Learning Analytics Research Articles

Orchid: a novel management, annotation and machine learning framework for analyzing cancer mutations.

As whole-genome tumor sequence and biological annotation datasets grow in size, number and content, there is an increasing basic science and clinical need for efficient and accurate data management and analysis software. With the emergence of increasingly sophisticated data stores, execution environments and machine learning algorithms, there is also a need for the integration of functionality across frameworks. We present orchid, a python based software package for the management, annotation and machine learning of cancer mutations. Building on technologies of parallel workflow execution, in-memory database storage and machine learning analytics, orchid efficiently handles millions of mutations and hundreds of features in an easy-to-use manner. We describe the implementation of orchid and demonstrate its ability to distinguish tissue of origin in 12 tumor types based on 339 features using a random forest classifier. Orchid and our annotated tumor mutation database are freely available at https://github.com/wittelab/orchid. Software is implemented in python 2.7, and makes use of MySQL or MemSQL databases. Groovy 2.4.5 is optionally required for parallel workflow execution. JWitte@ucsf.edu. Supplementary data are available at Bioinformatics online.

Data Mining, Machine Learning and Big Data Analytics

This paper analyses deep learning and traditional data mining and machine learning methods; compares the advantages and disadvantage of the traditional methods; introduces enterprise needs, systems and data, IT challenges, and Big Data in an extended service infrastructure. The feasibility and challenges of the applications of deep learning and traditional data mining and machine learning methods in Big Data analytics are also analyzed and presented.

On Fault Tolerance for Distributed Iterative Dataflow Processing

Large-scale graph and machine learning analytics widely employ distributed iterative processing. Typically, these analytics are a part of a comprehensive workflow, which includes data preparation, model building, and model evaluation. General-purpose distributed dataflow frameworks execute all steps of such workflows holistically. This holistic view enables these systems to reason about and automatically optimize the entire pipeline. Here, graph and machine learning analytics are known to incur a long runtime since they require multiple passes over the data until convergence is reached. Thus, fault tolerance and a fast-recovery from any intermittent failure is critical for efficient analysis. In this paper, we propose novel fault-tolerant mechanisms for graph and machine learning analytics that run on distributed dataflow systems. We seek to reduce checkpointing costs and shorten failure recovery times. For graph processing, rather than writing checkpoints that block downstream operators, our mechanism writes checkpoints in an unblocking manner that does not break pipelined tasks. In contrast to the conventional approach for unblocking checkpointing (e.g., that manage checkpoints independently for immutable datasets), we inject the checkpoints of mutable datasets into the iterative dataflow itself. Hence, our mechanism is iteration-aware by design. This simplifies the system architecture and facilitates coordinating checkpoint creation during iterative graph processing. Moreover, we are able to rapidly rebound, via confined recovery, by exploiting the fact that log files exist locally on healthy nodes and managing to avoid a complete recomputation from scratch. In addition, we propose replica recovery for machine learning algorithms, whereby we employ a broadcast variable that enables us to quickly recover without having to introduce any checkpoints. In order to evaluate our fault tolerance strategies, we conduct both a theoretical study and experimental analyses using Apache Flink and discover that they outperform blocking checkpointing and complete recovery.

Are Nomothetic or Ideographic Approaches Superior in Predicting Daily Exercise Behaviors?

The understanding of how stress influences health behavior can provide insights into developing healthy lifestyle interventions. This understanding is traditionally attained through observational studies that examine associations at a population level. This nomothetic approach, however, is fundamentally limited by the fact that the environment- person milieu that constitutes stress exposure and experience can vary substantially between individuals, and the modifiable elements of these exposures and experiences are individual-specific. With recent advances in smartphone and sensing technologies, it is now possible to conduct idiographic assessment in users' own environment, leveraging the full-range observations of actions and experiences that result in differential response to naturally occurring events. The aim of this paper is to explore the hypothesis that an ideographic N-of-1 model can better capture an individual's stress- behavior pathway (or the lack thereof) and provide useful person-specific predictors of exercise behavior. This paper used the data collected in an observational study in 79 participants who were followed for up to a 1-year period, wherein their physical activity was continuously and objectively monitored by actigraphy and their stress experience was recorded via ecological momentary assessment on a mobile app. In addition, our analyses considered exogenous and environmental variables retrieved from public archive such as day in a week, daylight time, temperature and precipitation. Leveraging the multiple data sources, we developed prediction algorithms for exercise behavior using random forest and classification tree techniques using a nomothetic approach and an N-of-1 approach. The two approaches were compared based on classification errors in predicting personalized exercise behavior. Eight factors were selected by random forest for the nomothetic decision model, which was used to predict whether a participant would exercise on a particular day. The predictors included previous exercise behavior, emotional factors (e.g., midday stress), external factors such as weather (e.g., temperature), and self-determination factors (e.g., expectation of exercise). The nomothetic model yielded an average classification error of 36%. The ideographic N-of-1 models used on average about two predictors for each individual, and had an average classification error of 25%, which represented an improvement of 11 percentage points. Compared to the traditional one-size-fits-all, nomothetic model that generalizes population-evidence for individuals, the proposed N-of-1 model can better capture the individual difference in their stressbehavior pathways. In this paper, we demonstrate it is feasible to perform personalized exercise behavior prediction, mainly made possible by mobile health technology and machine learning analytics.

Large Scale Graph Matching(LSGM): Techniques, Tools, Applications and Challenges

Large Scale Graph Matching (LSGM) is one of the fundamental problems in Graph theory and it has applications in many areas such as Computer Vision, Machine Learning, Pattern Recognition and Big Data Analytics (Data Science). Matching belongs to the combinatorial class of problems which refers to finding correspondence between the nodes of a graph or among set of graphs (subgraphs) either precisely or approximately. Precise Matching is also known as Exact Matching such as (sub)Graph Isomorphism and Approximate Matching is called Inexact Matching in which matching activity concerns with conceptual/semantic matching rather than focusing on structural details of graphs. In this article, a review of matching problem is presented i.e. Semantic Matching (conceptual), Syntactic Match-ing (structural) and Schematic Matching (Schema based). The aim is to present the current state of the art in Large Scale Graph Matching (LSGM), a systematic review of algorithms, tools and techniques along with the existing challenges of LSGM. Moreover, the potential application domains and related research activities are provided.

Comparing SQL and NoSQL approaches for clustering over big data

Data mining is the process of discovering patterns in large datasets. With the exponential growth of available information, new machine learning, statistics and other analytics techniques have to be developed to solve the processing needs required to do such analysis fast enough to be used successfully. In this study, techniques like cluster analysis are used over generated data in order to do customer segmentation, and the system performance is evaluated by measuring the processing time. The data used in the current paper is generated using the Star Schema Benchmark (SSB). Our main goal is to find a scalable solution to run data mining over a decision support benchmark. Four different systems will be tested: single node MySQL, MySQL cluster, Apache Mahout and R. By running MySQL cluster and Mahout, each system distributed by four nodes, the paper compares the performance of k-means run in parallel. MySQL and R will allow for comparison of this kind of execution against methods running on a single machine, both on relational and non-relational systems.