Data Retrieval and Sorting for Multidimensional Data Using Machine Learning in Big Data

How to retrieve accurately and quickely locate information from massive network data from the big data is focuse on data retrieve sorting optimization model.Based on traditional data retrieval sorting technology this work proposes multidimensional data retrieval and sorting considering the characteristic of data,users and application. This work use the financial microblog data retrieval base on real query intensions and financial tendency of the system. Finally this work shows the test results for multidimensional data retrieval and sorting using machine learning. In this thesis, proposed methodology has been presented. The proposed system is implemente using machine learning based on the multidimensional data characteristic. This proposed system is implemented in java . It shows soritng and retrieval result of the system based on the characteristic of multidimensional data

Euclidean Distance Matrices: Essential Theory, Algorithms and Applications

Euclidean distance matrices (EDM) are matrices of squared distances between points. The definition is deceivingly simple: thanks to their many useful properties they have found applications in psychometrics, crystallography, machine learning, wireless sensor networks, acoustics, and more. Despite the usefulness of EDMs, they seem to be insufficiently known in the signal processing community. Our goal is to rectify this mishap in a concise tutorial. We review the fundamental properties of EDMs, such as rank or (non)definiteness. We show how various EDM properties can be used to design algorithms for completing and denoising distance data. Along the way, we demonstrate applications to microphone position calibration, ultrasound tomography, room reconstruction from echoes and phase retrieval. By spelling out the essential algorithms, we hope to fast-track the readers in applying EDMs to their own problems. Matlab code for all the described algorithms, and to generate the figures in the paper, is available online. Finally, we suggest directions for further research.Comment: - 17 pages, 12 figures, to appear in IEEE Signal Processing Magazine - change of title in the last revisio

RAM: array processing over a relational DBMS

Developing multimedia applications in relational databases is hindered by a mismatch in computational frameworks. Efficient manipulation of multimedia data calls for array-based processing, which at best is available as a database add-on, not supported by the query optimizer. As a result, array-based processing ends up in dedicated programs outside the DBMS: non-reusable black boxes. The goal of our research is to reduce this gap between user-needs and system functionality by developing a seemless integration of array processing in a relational algebra engine. The paper introduces a declarative language for array-expressions based on the array comprehension, and its mapping to a relational kernel in a prototype implementation. The layered architecture of the resulting array database management system allows the use of structural knowledge available in the array data type. This additional source of information can be exploited for query optimization, which is demonstrated with a case study. The experiments show how the performance of a standard tool for matrix computations can be achieved without sacrificing data independence, highlighting however a critical aspect in the DBMS architecture proposed

CloudTree: A Library to Extend Cloud Services for Trees

In this work, we propose a library that enables on a cloud the creation and management of tree data structures from a cloud client. As a proof of concept, we implement a new cloud service CloudTree. With CloudTree, users are able to organize big data into tree data structures of their choice that are physically stored in a cloud. We use caching, prefetching, and aggregation techniques in the design and implementation of CloudTree to enhance performance. We have implemented the services of Binary Search Trees (BST) and Prefix Trees as current members in CloudTree and have benchmarked their performance using the Amazon Cloud. The idea and techniques in the design and implementation of a BST and prefix tree is generic and thus can also be used for other types of trees such as B-tree, and other link-based data structures such as linked lists and graphs. Preliminary experimental results show that CloudTree is useful and efficient for various big data applications

A data cube model for analysis of high volumes of ambient data

Ambient systems generate large volumes of data for many of their application areas with XML often the format for data exchange. As a result, large scale ambient systems such as smart cities require some form of optimization before different components can merge their data streams. In data warehousing, the cube structure is often used for optimizing the analytics process with more recent structures such as dwarf, providing new orders of magnitude in terms of optimizing data extraction. However, these systems were developed for relational data and as a result, we now present the development of an XML dwarf to manage ambient systems generating XML data

Large Spatial Database Indexing with aX-tree

Spatial databases are optimized for the management of data stored based on their geometric space. Researchers through high degree scalability have proposed several spatial indexing structures towards this effect. Among these indexing structures is the X-tree. The existing X-trees and its variants are designed for dynamic environment, with the capability for handling insertions and deletions. Notwithstanding, the X-tree degrades on retrieval performance as dimensionality increases and brings about poor worst-case performance than sequential scan. We propose a new X-tree packing techniques for static spatial databases which performs better in space utilization through cautious packing. This new improved structure yields two basic advantage: It reduces the space overhead of the index and produces a better response time, because the aX-tree has a higher fan-out and so the tree always ends up shorter. New model for super-node construction and effective method for optimal packing using an improved str bulk-loading technique is proposed. The study reveals that proposed system performs better than many existing spatial indexing structure

GPU LSM: A Dynamic Dictionary Data Structure for the GPU

We develop a dynamic dictionary data structure for the GPU, supporting fast insertions and deletions, based on the Log Structured Merge tree (LSM). Our implementation on an NVIDIA K40c GPU has an average update (insertion or deletion) rate of 225 M elements/s, 13.5x faster than merging items into a sorted array. The GPU LSM supports the retrieval operations of lookup, count, and range query operations with an average rate of 75 M, 32 M and 23 M queries/s respectively. The trade-off for the dynamic updates is that the sorted array is almost twice as fast on retrievals. We believe that our GPU LSM is the first dynamic general-purpose dictionary data structure for the GPU.Comment: 11 pages, accepted to appear on the Proceedings of IEEE International Parallel and Distributed Processing Symposium (IPDPS'18