Is there a need for on-chip photonic integration for large data warehouse switches

2012 ◽  
Author(s):  
Ali Ghiasi
Keyword(s):  
Data Warehouse ◽  
Large Data ◽  
Photonic Integration ◽  
On Chip

scholarly journals A Pattern Storage System using Pattern Warehouse along with Sources of Pattern Generation and Applications

2019 ◽  
Vol 8 (10S) ◽  
pp. 357-362
Keyword(s):  
Data Mining ◽  
Data Warehouse ◽  
Storage System ◽  
Critical Evaluation ◽  
Large Data ◽  
Vital Role ◽  
Pattern Generation ◽  
Data Repository ◽  
Storage Unit ◽  
Data Mining Algorithms

Now a day different data mining algorithms are ready to create the specific set of data known as Pattern from a huge data repository, but there is no infrastructure or system to save it as persistent storage for the generated patterns. Pattern warehouse presents a foundation to make these patterns safe in the specific environment for long term use. Most organizations are excited to know the information or patterns rather than raw data or group of unprocessed data. Because extracted knowledge play a vital role to take right decision for the growth of an organization. We have examined the sources of patterns generated from large data sets. In this paper, we have presented little importance on the application area of pattern and idea of patter warehouse, the architecture of pattern warehouse then correlation between data warehouse and data mining, association between data mining and pattern warehouse, critical evaluation between existing approaches which theoretically published and more stress on association rule related review elements. In this paper, we analyze the patterns warehouse, data warehouse concerning various factors like storage space, type of storage unit, characteristics, and provide several research domains.

scholarly journals Computing Sorted Subsets for Data Processing in Communicating Software/Hardware Control Systems

2015 ◽  
Vol 11 (1) ◽  
pp. 126 ◽  
Author(s):  
Valery Sklyarov ◽  
Iouliia Skliarova ◽  
Artjom Rjabov ◽  
Alexander Sudnitson
Keyword(s):  
High Performance ◽  
Large Data ◽  
Computation Method ◽  
Data Sets ◽  
Processing Unit ◽  
Programmable Logic ◽  
Practical Applications ◽  
Sorting Networks ◽  
Hardware System ◽  
On Chip

Computing and filtering sorted subsets are frequently required in statistical data manipulation and control applications. The main objective is to extract subsets from large data sets in accordance with some criteria, for example, with the maximum and/or the minimum values in the entire set or within the predefined constraints. The paper suggests a new computation method enabling the indicated above problem to be solved in all programmable systems-on-chip from the Xilinx Zynq family that combine a dual-core Cortex-A9 processing unit and programmable logic linked by high-performance interfaces. The method involves highly parallel sorting networks and run-time filtering. The computations are done in communicating software, running in the processing unit, and hardware, implemented in the programmable logic. Practical applications of the proposed technique are also shown. The results of implementation and experiments clearly demonstrate significant speed-up of the developed software/hardware system comparing to alternative software implementations.

Materialized View Selection using Marriage in Honey Bees Optimization

2015 ◽  
Vol 5 (3) ◽  
pp. 1-25 ◽  
Author(s):  
Biri Arun ◽  
T.V. Vijay Kumar
Keyword(s):  
Data Warehouse ◽  
Honey Bees ◽  
Response Times ◽  
Large Data ◽  
Strategic Decision ◽  
Selection Algorithm ◽  
View Selection ◽  
Materialized View Selection ◽  
On Line ◽  
Selection Of

Data warehouse was designed to cater to the strategic decision making needs of an organization. Most queries posed on them are on-line analytical queries, which are complex and computation intensive in nature and have high query response times when processed against a large data warehouse. This time can be substantially reduced by materializing pre-computed summarized views and storing them in a data warehouse. All possible views cannot be materialized due to storage space constraints. Also, an optimal selection of subsets of views is shown to be an NP-Complete problem. This problem of view selection has been addressed in this paper by selecting a beneficial set of views, from amongst all possible views, using the swarm intelligence technique Marriage in Honey Bees Optimization (MBO). An MBO based view selection algorithm (MBOVSA), which aims to select views that incur the minimum total cost of evaluating all the views (TVEC), is proposed. In MBOVSA, the search has been intensified by incorporating the royal jelly feeding phase into MBO. MBOVSA, when compared with the most fundamental greedy based view selection algorithm HRUA, is able to select comparatively better quality views.

Use of Sensor Data Warehouse for Soil Moisture Analysis

2016 ◽  
pp. 43-57
Author(s):  
Myoung-Ah Kang ◽  
François Pinet ◽  
Sandro Bimonte ◽  
Gil De Sousa ◽  
Jean-Pierre Chanet
Keyword(s):  
Data Warehouse ◽  
Spatial Data ◽  
Spatial Scales ◽  
Large Data ◽  
Sensor Nodes ◽  
Sensor Data ◽  
Data Sets ◽  
Innovative Solution ◽  
The Cost ◽  
General Architecture

More and more data are collected via sensors. Wireless networks can be implemented to facilitate the collection of sensors data and to reduce the cost of their acquisition. In this chapter, we present a general architecture combining Wireless Sensor Network (WSN) and Spatial Data Warehouse (SDW) technologies. This innovative solution is used to collect automatically sensor's information and to facilitate the analysis of these data. The WSN used in this application has been deployed by Irstea and tested during several years in vineyards in South of France. The novel contribution presented in this chapter is related to the use of a SDW to manage data produced by geo-referenced sensor nodes. SDW is one of the most appropriate modern technologies for analyzing large data sets at different temporal and spatial scales. This type of databases is a specific category of information system used to integrate, accumulate and analyze information from various sources. These data are usually organized according to a multidimensional schema to facilitate the calculation of indicators. In this chapter, we introduce the development of a SDW storing the data collected by this WSN. The implemented data warehouse can allow users to aggregate and explore interactively data produced by sensors. With this system, it is possible to visualize on a map the results of these aggregations.

Multi-dimensional spatial light communication made with on-chip InGaN photonic integration

2017 ◽  
Vol 66 ◽  
pp. 659-663 ◽  
Author(s):  
Yongchao Yang ◽  
Bingcheng Zhu ◽  
Zheng Shi ◽  
Jinyuan Wang ◽  
Xin Li ◽  
...  
Keyword(s):  
Photonic Integration ◽  
On Chip

A New Architecture for Large Data Warehouse

2011 ◽  
Vol 55-57 ◽  
pp. 87-90
Author(s):  
Gang Li ◽  
Xing San Qian ◽  
Chun Ming Ye
Keyword(s):  
Decision Making ◽  
Data Warehouse ◽  
Business Intelligence ◽  
High Performance ◽  
Lower Cost ◽  
Large Data ◽  
Current Data

Data warehouse is playing a more and more important role in company’s decision making; it is the basis for a typical business intelligence solution. The paper points out the reasons why data warehouse projects failed and by analyzing the current data warehouse architectures, as well as technologies used in industry, a new data warehouse architecture is proposed which has many advantages over current ones, for example, it is extensible, reusable, flexible and with high performance and lower cost.

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