DHLP 1&2: Giraph based distributed label propagation algorithms on heterogeneous drug-related networks

Background and Objective: Heterogeneous complex networks are large graphs consisting of different types of nodes and edges. The knowledge extraction from these networks is complicated. Moreover, the scale of these networks is steadily increasing. Thus, scalable methods are required. Methods: In this paper, two distributed label propagation algorithms for heterogeneous networks, namely DHLP-1 and DHLP-2 have been introduced. Biological networks are one type of the heterogeneous complex networks. As a case study, we have measured the efficiency of our proposed DHLP-1 and DHLP-2 algorithms on a biological network consisting of drugs, diseases, and targets. The subject we have studied in this network is drug repositioning but our algorithms can be used as general methods for heterogeneous networks other than the biological network. Results: We compared the proposed algorithms with similar non-distributed versions of them namely MINProp and Heter-LP. The experiments revealed the good performance of the algorithms in terms of running time and accuracy.Comment: Source code available for Apache Giraph on Hadoo

Large-Scale Image Processing Using MapReduce

Jälgides tänapäeva tehnoloogia arengut ning odavate fotokaamerate üha laialdasemat levikut, on üha selgem, et ühe osa üha kasvavast inimeste tekitatud andmete hulgast moodustavad pildid. Teades, et tõenäoliselt tuleb neid andmeid ka töödelda, ning et üksikute arvutite võimsus ei luba kohati juba praegu neid mahukamate ülesannete jaoks kasutada, on inimesed hakanud uurima mitmete hajusarvutuse mudelite pakutavaid võimalusi. Üks selline on MapReduce, mille põhiliseks aluseks on arvutuste üldisele kujule viimine, seades programmeerija ülesandeks defineerida vaid selle, mis toimub andmetega nelja arvutuse faasi - Input, Map, Reduce, Output - jooksul. Kuna sellest mudelist on olemas kvaliteetseid vabavara realisatsioone, ning mahukamateks arvutusteks on kerge vaeva ja vähese kuluga võimalik rentida vajalik infrastruktuur, siis on selline lähenemine pilditöötlusele muutunud peaaegu igaühele kättesaadavaks. Antud magistritöö eesmärgiks on uurida MapReduce mudeli kasutatavust suuremahulise pilditöötluse vallas. Selleks vaatlen eraldi juhte, kus tegemist on tavalistest piltidest koosneva suure andmestikuga, ning kus tuleb töödelda ühte suuremahulist pilti. Samuti jagan nelja klassi vahel kõik pilditöötlusalgoritmid, nimetades need vastavalt lokaalseteks, iteratiivseteks lokaalseteks, mittelokaalseteks ja iteratiivseteks mittelokaalseteks algoritmideks. Kasutades neid jaotusi, kirjeldan üldiselt põhilisi probleeme ja takistusi, mis võivad segada mingit tüüpi algoritmide hajusat rakendamist mingit tüüpi piltandmetel, ning pakun välja võimalikke lahendusi. Töö praktilises osas kirjeldan MapReduce mudeli kasutamist Apache Hadoop raamistikuga kahel erineval andmestikul, millest esimene on 265GiB-suurune pildikogu, ning teine 6.99 gigapiksli suurune mikroskoobifoto. Esimese näite puhul on ülesandeks pildikogust meta-andmete eraldamine, kasutades selleks objekti- ning tekstituvastust. Teise andmestiku puhul on ülesandeks töödelda pilti ühe kindla mitteiteratiivse lokaalse algoritmiga. Kuigi mõlemal juhul on tegemist vaid katsetamise eesmärgil loodud rakendustega, on mõlemal puhul näha, et olemasolevate pilditöötluse algoritmide MapReduce programmideks teisendamine on küllaltki lihtne, ning ei too endaga kaasa suuri kadusid jõudluses. Kokkuvõtteks väidan, et tavapärases mõõdus piltidest koosnevate andmestike puhul on MapReduce mudel lihtne viis arvutusi hajusale kujule viies kiirendada, kuid suuremahuliste piltide puhul kehtib see enamasti ainult mitteiteratiivsete lokaalsete algoritmidega.Due to the increasing popularity of cheap digital photography equipment, personal computing devices with easy to use cameras, and an overall im- provement of image capture technology with regard to quality, the amount of data generated by people each day shows trends of growing faster than the processing capabilities of single devices. For other tasks related to large-scale data, humans have already turned towards distributed computing as a way to side-step impending physical limitations to processing hardware by com- bining the resources of many computers and providing programmers various different interfaces to the resulting construct, relieving them from having to account for the intricacies stemming from it’s physical structure. An example of this is the MapReduce model, which - by way of placing all calculations to a string of Input-Map-Reduce-Output operations capable of working in- dependently - allows for easy application of distributed computing for many trivially parallelised processes. With the aid of freely available implemen- tations of this model and cheap computing infrastructure offered by cloud providers, having access to expensive purpose-built hardware or in-depth un- derstanding of parallel programming are no longer required of anyone who wishes to work with large-scale image data. In this thesis, I look at the issues of processing two kinds of such data - large data-sets of regular images and single large images - using MapReduce. By further classifying image pro- cessing algorithms to iterative/non-iterative and local/non-local, I present a general analysis on why different combinations of algorithms and data might be easier or harder to adapt for distributed processing with MapReduce. Finally, I describe the application of distributed image processing on two ex- ample cases: a 265GiB data-set of photographs and a 6.99 gigapixel image. Both preliminary analysis and practical results indicate that the MapReduce model is well suited for distributed image processing in the first case, whereas in the second case, this is true for only local non-iterative algorithms, and further work is necessary in order to provide a conclusive decision

Toward High-Performance Computing and Big Data Analytics Convergence: The Case of Spark-DIY

Convergence between high-performance computing (HPC) and big data analytics (BDA) is currently an established research area that has spawned new opportunities for unifying the platform layer and data abstractions in these ecosystems. This work presents an architectural model that enables the interoperability of established BDA and HPC execution models, reflecting the key design features that interest both the HPC and BDA communities, and including an abstract data collection and operational model that generates a unified interface for hybrid applications. This architecture can be implemented in different ways depending on the process- and data-centric platforms of choice and the mechanisms put in place to effectively meet the requirements of the architecture. The Spark-DIY platform is introduced in the paper as a prototype implementation of the architecture proposed. It preserves the interfaces and execution environment of the popular BDA platform Apache Spark, making it compatible with any Spark-based application and tool, while providing efficient communication and kernel execution via DIY, a powerful communication pattern library built on top of MPI. Later, Spark-DIY is analyzed in terms of performance by building a representative use case from the hydrogeology domain, EnKF-HGS. This application is a clear example of how current HPC simulations are evolving toward hybrid HPC-BDA applications, integrating HPC simulations within a BDA environment.This work was supported in part by the Spanish Ministry of Economy, Industry and Competitiveness under Grant TIN2016-79637-P(toward Unification of HPC and Big Data Paradigms), in part by the Spanish Ministry of Education under Grant FPU15/00422 TrainingProgram for Academic and Teaching Staff Grant, in part by the Advanced Scientific Computing Research, Office of Science, U.S.Department of Energy, under Contract DE-AC02-06CH11357, and in part by the DOE with under Agreement DE-DC000122495,Program Manager Laura Biven

Contextual Anomaly Detection Framework for Big Sensor Data

Performing predictive modelling, such as anomaly detection, in Big Data is a difficult task. This problem is compounded as more and more sources of Big Data are generated from environmental sensors, logging applications, and the Internet of Things. Further, most current techniques for anomaly detection only consider the content of the data source, i.e. the data itself, without concern for the context of the data. As data becomes more complex it is increasingly important to bias anomaly detection techniques for the context, whether it is spatial, temporal, or semantic. The work proposed in this thesis outlines a contextual anomaly detection framework for use in Big sensor Data systems. The framework uses a well-defined content anomaly detection algorithm for real-time point anomaly detection. Additionally, we present a post-processing context-aware anomaly detection algorithm based on sensor profiles, which are groups of contextually similar sensors generated by a multivariate clustering algorithm. The contextual anomaly detection framework is evaluated with respect to two different Big sensor Data data sets; one for electrical sensors, and another for temperature sensors within a building

Scalable Scientific Computing Algorithms Using MapReduce

Cloud computing systems, like MapReduce and Pregel, provide a scalable and fault tolerant environment for running computations at massive scale. However, these systems are designed primarily for data intensive computational tasks, while a large class of problems in scientific computing and business analytics are computationally intensive (i.e., they require a lot of CPU in addition to I/O). In this thesis, we investigate the use of cloud computing systems, in particular MapReduce, for computationally intensive problems, focusing on two classic problems that arise in scienti c computing and also in analytics: maximum clique and matrix inversion. The key contribution that enables us to e ectively use MapReduce to solve the maximum clique problem on dense graphs is a recursive partitioning method that partitions the graph into several subgraphs of similar size and running time complexity. After partitioning, the maximum cliques of the di erent partitions can be computed independently, and the computation is sped up using a branch and bound method. Our experiments show that our approach leads to good scalability, which is unachievable by other partitioning methods since they result in partitions of di erent sizes and hence lead to load imbalance. Our method is more scalable than an MPI algorithm, and is simpler and more fault tolerant. For the matrix inversion problem, we show that a recursive block LU decomposition allows us to e ectively compute in parallel both the lower triangular (L) and upper triangular (U) matrices using MapReduce. After computing the L and U matrices, their inverses are computed using MapReduce. The inverse of the original matrix, which is the product of the inverses of the L and U matrices, is also obtained using MapReduce. Our technique is the rst matrix inversion technique that uses MapReduce. We show experimentally that our technique has good scalability, and it is simpler and more fault tolerant than MPI implementations such as ScaLAPACK

Big data analytics for large-scale wireless networks: Challenges and opportunities

© 2019 Association for Computing Machinery. The wide proliferation of various wireless communication systems and wireless devices has led to the arrival of big data era in large-scale wireless networks. Big data of large-scale wireless networks has the key features of wide variety, high volume, real-time velocity, and huge value leading to the unique research challenges that are different from existing computing systems. In this article, we present a survey of the state-of-art big data analytics (BDA) approaches for large-scale wireless networks. In particular, we categorize the life cycle of BDA into four consecutive stages: Data Acquisition, Data Preprocessing, Data Storage, and Data Analytics. We then present a detailed survey of the technical solutions to the challenges in BDA for large-scale wireless networks according to each stage in the life cycle of BDA. Moreover, we discuss the open research issues and outline the future directions in this promising area

On Applications of Relational Data

With the advances of technology and the popularity of the Internet, a large amount of data is being generated and collected. Much of these data is relational data, which describe how people and things, or entities, are related to one another. For example, data from sale transactions on e-commerce websites tell us which customers buy or view which products. Analyzing the known relationships from relational data can help us to discover knowledge that can benefit businesses, organizations, and our lives. For instance, learning the products that are commonly bought together allows businesses to recommend products to customers and increase their sales. Hidden or new relationships can also be inferred based on relational data. In addition, based on the connections among the entities, we can approximate the level of relatedness between two entities, even though their relationship may be hard to observe or quantify. This research aims to explore novel applications of relational data that will help to improve our life in various aspects, such as improving business operations, improving experiences in using online services, and improving health care services. In applying relational data in any domain, there are two common challenges. First, the size of the data can be massive, but many applications require that results are obtained within a short time. Second, relational data are often noisy and incomplete. Many relationships are extracted automatically from text resources, and hence they are prone to errors. Our goal is not only to propose novel applications of relational data but also to develop techniques and algorithms that will facilitate and make such applications practical. This work addresses three novel applications of relational data. The first application is to use relational data to improve user experiences in online video sharing services. Second, we propose the use of relational data to find entities that are closely related to one another. Such problems arise in various domains, such as product recommendation and query suggestion. Third, we propose the use of relational data to assist medical practitioners in drug prescription. For these applications, we introduce several techniques and algorithms to address the aforementioned challenges in using relational data. Our approaches are evaluated extensively to demonstrate their effectiveness. The approaches proposed in this work not only can be used in the specific applications we discuss but also can help to facilitate and promote the use of relational data in other application domains