A MapReduce Based Distributed LSI for Scalable Information Retrieval

Latent Semantic Indexing (LSI) has been widely used in information retrieval due to its efficiency in solving the problems of polysemy and synonymy. However, LSI is notably a computationally intensive process because of the computing complexities of singular value decomposition and filtering operations involved in the process. This paper presents MR-LSI, a MapReduce based distributed LSI algorithm for scalable information retrieval. The performance of MR-LSI is first evaluated in a small scale experimental cluster environment, and subsequently evaluated in large scale simulation environments. By partitioning the dataset into smaller subsets and optimizing the partitioned subsets across a cluster of computing nodes, the overhead of the MR-LSI algorithm is reduced significantly while maintaining a high level of accuracy in retrieving documents of user interest. A genetic algorithm based load balancing scheme is designed to optimize the performance of MR-LSI in heterogeneous computing environments in which the computing nodes have varied resources

A resource aware distributed LSI algorithm for scalable information retrieval

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Latent Semantic Indexing (LSI) is one of the popular techniques in the information retrieval fields. Different from the traditional information retrieval techniques, LSI is not based on the keyword matching simply. It uses statistics and algebraic computations. Based on Singular Value Decomposition (SVD), the higher dimensional matrix is converted to a lower dimensional approximate matrix, of which the noises could be filtered. And also the issues of synonymy and polysemy in the traditional techniques can be overcome based on the investigations of the terms related with the documents. However, it is notable that LSI suffers a scalability issue due to the computing complexity of SVD. This thesis presents a resource aware distributed LSI algorithm MR-LSI which can solve the scalability issue using Hadoop framework based on the distributed computing model MapReduce. It also solves the overhead issue caused by the involved clustering algorithm. The evaluations indicate that MR-LSI can gain significant enhancement compared to the other strategies on processing large scale of documents. One remarkable advantage of Hadoop is that it supports heterogeneous computing environments so that the issue of unbalanced load among nodes is highlighted. Therefore, a load balancing algorithm based on genetic algorithm for balancing load in static environment is proposed. The results show that it can improve the performance of a cluster according to heterogeneity levels. Considering dynamic Hadoop environments, a dynamic load balancing strategy with varying window size has been proposed. The algorithm works depending on data selecting decision and modeling Hadoop parameters and working mechanisms. Employing improved genetic algorithm for achieving optimized scheduler, the algorithm enhances the performance of a cluster with certain heterogeneity levels

High performance latent dirichlet allocation for text mining

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Latent Dirichlet Allocation (LDA), a total probability generative model, is a three-tier Bayesian model. LDA computes the latent topic structure of the data and obtains the significant information of documents. However, traditional LDA has several limitations in practical applications. LDA cannot be directly used in classification because it is a non-supervised learning model. It needs to be embedded into appropriate classification algorithms. LDA is a generative model as it normally generates the latent topics in the categories where the target documents do not belong to, producing the deviation in computation and reducing the classification accuracy. The number of topics in LDA influences the learning process of model parameters greatly. Noise samples in the training data also affect the final text classification result. And, the quality of LDA based classifiers depends on the quality of the training samples to a great extent. Although parallel LDA algorithms are proposed to deal with huge amounts of data, balancing computing loads in a computer cluster poses another challenge. This thesis presents a text classification method which combines the LDA model and Support Vector Machine (SVM) classification algorithm for an improved accuracy in classification when reducing the dimension of datasets. Based on Density-Based Spatial Clustering of Applications with Noise (DBSCAN), the algorithm automatically optimizes the number of topics to be selected which reduces the number of iterations in computation. Furthermore, this thesis presents a noise data reduction scheme to process noise data. When the noise ratio is large in the training data set, the noise reduction scheme can always produce a high level of accuracy in classification. Finally, the thesis parallelizes LDA using the MapReduce model which is the de facto computing standard in supporting data intensive applications. A genetic algorithm based load balancing algorithm is designed to balance the workloads among computers in a heterogeneous MapReduce cluster where the computers have a variety of computing resources in terms of CPU speed, memory space and hard disk space

A resource aware distributed LSI algorithm for scalable information retrieval

Latent Semantic Indexing (LSI) is one of the popular techniques in the information retrieval fields. Different from the traditional information retrieval techniques, LSI is not based on the keyword matching simply. It uses statistics and algebraic computations. Based on Singular Value Decomposition (SVD), the higher dimensional matrix is converted to a lower dimensional approximate matrix, of which the noises could be filtered. And also the issues of synonymy and polysemy in the traditional techniques can be overcome based on the investigations of the terms related with the documents. However, it is notable that LSI suffers a scalability issue due to the computing complexity of SVD. This thesis presents a resource aware distributed LSI algorithm MR-LSI which can solve the scalability issue using Hadoop framework based on the distributed computing model MapReduce. It also solves the overhead issue caused by the involved clustering algorithm. The evaluations indicate that MR-LSI can gain significant enhancement compared to the other strategies on processing large scale of documents. One remarkable advantage of Hadoop is that it supports heterogeneous computing environments so that the issue of unbalanced load among nodes is highlighted. Therefore, a load balancing algorithm based on genetic algorithm for balancing load in static environment is proposed. The results show that it can improve the performance of a cluster according to heterogeneity levels. Considering dynamic Hadoop environments, a dynamic load balancing strategy with varying window size has been proposed. The algorithm works depending on data selecting decision and modeling Hadoop parameters and working mechanisms. Employing improved genetic algorithm for achieving optimized scheduler, the algorithm enhances the performance of a cluster with certain heterogeneity levels.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

A survey on big data indexing strategies

The operations of the Internet have led to a significant growth and accumulation of data known as Big Data.Individuals and organizations that utilize this data, had no idea, nor were they prepared for this data explosion.Hence, the available solutions cannot meet the needs of the growing heterogeneous data in terms of processing. This results in inefficient information retrieval or search query results.The design of indexing strategies that can support this need is required. A survey on various indexing strategies and how they are utilized for solving Big Data management issues can serve as a guide for choosing the strategy best suited for a problem, and can also serve as a base for the design of more efficient indexing strategies.The aim of the study is to explore the characteristics of the indexing strategies used in Big Data manageability by covering some of the weaknesses and strengths of B-tree, R-tree, to name but a few. This paper covers some popular indexing strategies used for Big Data management. It exposes the potentials of each by carefully exploring their properties in ways that are related to problem solving

Gradient Coding from Cyclic MDS Codes and Expander Graphs

Gradient coding is a technique for straggler mitigation in distributed learning. In this paper we design novel gradient codes using tools from classical coding theory, namely, cyclic MDS codes, which compare favorably with existing solutions, both in the applicable range of parameters and in the complexity of the involved algorithms. Second, we introduce an approximate variant of the gradient coding problem, in which we settle for approximate gradient computation instead of the exact one. This approach enables graceful degradation, i.e., the $\ell_2$ error of the approximate gradient is a decreasing function of the number of stragglers. Our main result is that normalized adjacency matrices of expander graphs yield excellent approximate gradient codes, which enable significantly less computation compared to exact gradient coding, and guarantee faster convergence than trivial solutions under standard assumptions. We experimentally test our approach on Amazon EC2, and show that the generalization error of approximate gradient coding is very close to the full gradient while requiring significantly less computation from the workers

Hierarchical Filter and Refinement System Over Large Polygonal Datasets on CPU-GPU

In this paper, we introduce our hierarchical filter and refinement technique that we have developed for parallel geometric intersection operations involving large polygons and polylines. The inputs are two layers of large polygonal datasets and the computations are spatial intersection on a pair of cross-layer polygons. These intersections are the compute-intensive spatial data analytic kernels in spatial join and map overlay computations. We have extended the classical filter and refine algorithms using PolySketch Filter to improve the performance of geospatial computations. In addition to filtering polygons by their Minimum Bounding Rectangle (MBR), our hierarchical approach explores further filtering using tiles (smaller MBRs) to increase the effectiveness of filtering and decrease the computational workload in the refinement phase. We have implemented this filter and refine system on CPU and GPU by using OpenMP and OpenACC. After using R-tree, on average, our filter technique can still discard 69% of polygon pairs which do not have segment intersection points. PolySketch filter reduces on average 99.77% of the workload of finding line segment intersections. PNP based task reduction and Striping algorithms filter out on average 95.84% of the workload of Point-in-Polygon tests. Our CPU-GPU system performs spatial join on two shapefiles, namely USA Water Bodies and USA Block Group Boundaries with 683K polygons in about 10 seconds using NVidia Titan V and Titan Xp GPU