High-dimensional indexing methods utilizing clustering and dimensionality reduction

The emergence of novel database applications has resulted in the prevalence of a new paradigm for similarity search. These applications include multimedia databases, medical imaging databases, time series databases, DNA and protein sequence databases, and many others. Features of data objects are extracted and transformed into high-dimensional data points. Searching for objects becomes a search on points in the high-dimensional feature space. The dissimilarity between two objects is determined by the distance between two feature vectors. Similarity search is usually implemented as nearest neighbor search in feature vector spaces. The cost of processing k-nearest neighbor (k-NN) queries via a sequential scan increases as the number of objects and the number of features increase. A variety of multi-dimensional index structures have been proposed to improve the efficiency of k-NN query processing, which work well in low-dimensional space but lose their efficiency in high-dimensional space due to the curse of dimensionality. This inefficiency is dealt in this study by Clustering and Singular Value Decomposition - CSVD with indexing, Persistent Main Memory - PMM index, and Stepwise Dimensionality Increasing - SDI-tree index. CSVD is an approximate nearest neighbor search method. The performance of CSVD with indexing is studied and the approximation to the distance in original space is investigated. For a given Normalized Mean Square Error - NMSE, the higher the degree of clustering, the higher the recall. However, more clusters require more disk page accesses. Certain number of clusters can be obtained to achieve a higher recall while maintaining a relatively lower query processing cost. Clustering and Indexing using Persistent Main Memory - CIPMM framework is motivated by the following consideration: (a) a significant fraction of index pages are accessed randomly, incurring a high positioning time for each access; (b) disk transfer rate is improving 40% annually, while the improvement in positioning time is only 8%; (c) query processing incurs less CPU time for main memory resident than disk resident indices. CIPMM aims at reducing the elapsed time for query processing by utilizing sequential, rather than random disk accesses. A specific instance of the CIPMM framework CIPOP, indexing using Persistent Ordered Partition - OP-tree, is elaborated and compared with clustering and indexing using the SR-tree, CISR. The results show that CIPOP outperforms CISR, and the higher the dimensionality, the higher the performance gains. The SDI-tree index is motivated by fanouts decrease with dimensionality increasing and shorter vectors reduce cache misses. The index is built by using feature vectors transformed via principal component analysis, resulting in a structure with fewer dimensions at higher levels and increasing the number of dimensions from one level to the other. Dimensions are retained in nonincreasing order of their variance according to a parameter p, which specifies the incremental fraction of variance at each level of the index. Experiments on three datasets have shown that SDL-trees with carefully tuned parameters access fewer disk accesses than SR-trees and VAMSR-trees and incur less CPU time than VA-Files in addition

LogBase: A Scalable Log-structured Database System in the Cloud

Numerous applications such as financial transactions (e.g., stock trading) are write-heavy in nature. The shift from reads to writes in web applications has also been accelerating in recent years. Write-ahead-logging is a common approach for providing recovery capability while improving performance in most storage systems. However, the separation of log and application data incurs write overheads observed in write-heavy environments and hence adversely affects the write throughput and recovery time in the system. In this paper, we introduce LogBase - a scalable log-structured database system that adopts log-only storage for removing the write bottleneck and supporting fast system recovery. LogBase is designed to be dynamically deployed on commodity clusters to take advantage of elastic scaling property of cloud environments. LogBase provides in-memory multiversion indexes for supporting efficient access to data maintained in the log. LogBase also supports transactions that bundle read and write operations spanning across multiple records. We implemented the proposed system and compared it with HBase and a disk-based log-structured record-oriented system modeled after RAMCloud. The experimental results show that LogBase is able to provide sustained write throughput, efficient data access out of the cache, and effective system recovery.Comment: VLDB201

Mixing multi-core CPUs and GPUs for scientific simulation software

Recent technological and economic developments have led to widespread availability of multi-core CPUs and specialist accelerator processors such as graphical processing units (GPUs). The accelerated computational performance possible from these devices can be very high for some applications paradigms. Software languages and systems such as NVIDIA's CUDA and Khronos consortium's open compute language (OpenCL) support a number of individual parallel application programming paradigms. To scale up the performance of some complex systems simulations, a hybrid of multi-core CPUs for coarse-grained parallelism and very many core GPUs for data parallelism is necessary. We describe our use of hybrid applica- tions using threading approaches and multi-core CPUs to control independent GPU devices. We present speed-up data and discuss multi-threading software issues for the applications level programmer and o er some suggested areas for language development and integration between coarse-grained and ne-grained multi-thread systems. We discuss results from three common simulation algorithmic areas including: partial di erential equations; graph cluster metric calculations and random number generation. We report on programming experiences and selected performance for these algorithms on: single and multiple GPUs; multi-core CPUs; a CellBE; and using OpenCL. We discuss programmer usability issues and the outlook and trends in multi-core programming for scienti c applications developers

URL Recommender using Parallel Processing

The main purpose of this project is to section similar news and articles from a vast variety of news articles. Let’s say, you want to read about latest news related to particular topic like sports. Usually, user goes to a particular website and goes through some news but he won’t be able to cover all the news coverage in a single website. So, he would be going through some other news website to checking it out and this continues. Also, some news websites might be containing some old news and the user might be going through that. To solve this, I have developed a web application where in user can see all the latest news from different websites in a single place. Users are given choice to select the news websites from which they want to view the latest news. The articles which we get from news websites are very random and we will be applying the DBSCAN algorithm and place the news articles in the cluster form for each specific topic for user to view. If the user wants to see sports, he will be provided with sports news section. And this process of extracting random news articles and forming news clusters are done at run time and at all times we will get the latest news as we will be extracting the data from web at run time. This is an effective way to watch all news at single place. And in turn this can be used as articles (URL) recommender as the user has to just go through the specific cluster which interests him and not visit all news websites to find articles. This way the user does not have to visit different sites to view all latest news. This idea can be expanded to not just news articles but also in other areas like collecting statistics of financial information etc. As the processing is done at runtime, the performance has to be improved. To improve the performance, the distributed data mining is used and multiple servers are being used which communicate with each other

Massively Parallel Sort-Merge Joins in Main Memory Multi-Core Database Systems

Two emerging hardware trends will dominate the database system technology in the near future: increasing main memory capacities of several TB per server and massively parallel multi-core processing. Many algorithmic and control techniques in current database technology were devised for disk-based systems where I/O dominated the performance. In this work we take a new look at the well-known sort-merge join which, so far, has not been in the focus of research in scalable massively parallel multi-core data processing as it was deemed inferior to hash joins. We devise a suite of new massively parallel sort-merge (MPSM) join algorithms that are based on partial partition-based sorting. Contrary to classical sort-merge joins, our MPSM algorithms do not rely on a hard to parallelize final merge step to create one complete sort order. Rather they work on the independently created runs in parallel. This way our MPSM algorithms are NUMA-affine as all the sorting is carried out on local memory partitions. An extensive experimental evaluation on a modern 32-core machine with one TB of main memory proves the competitive performance of MPSM on large main memory databases with billions of objects. It scales (almost) linearly in the number of employed cores and clearly outperforms competing hash join proposals - in particular it outperforms the "cutting-edge" Vectorwise parallel query engine by a factor of four.Comment: VLDB201