Data Warehousing

Hobbs, Lilian; Hillson, Susan; Lawande, Shilpa; Smith, Pete

doi:10.1016/b978-155558322-4/50003-5

Cited by 5 publications

(5 citation statements)

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“…Structural Query Language (SQL) is used as the basic tool for searching the MSD and the Oracle Relational Database Management System (RDBMS). It has features that dramatically improve query performance, such as Bitmap indexes, packages and functions, as well as optimizer hints 32. Relational algebra set operations (union, exclude, intersect) are used with a fast parallel implementation in the RDBMS.…”

Section: Methodsmentioning

confidence: 99%

MSDsite: A database search and retrieval system for the analysis and viewing of bound ligands and active sites

et al. 2004

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The three-dimensional environments of ligand binding sites have been derived from the parsing and loading of the PDB entries into a relational database. For each bound molecule the biological assembly of the quaternary structure has been used to determine all contact residues and a fast interactive search and retrieval system has been developed. Prosite pattern and short sequence search options are available together with a novel graphical query generator for inter-residue contacts. The database and its query interface are accessible from the Internet through a web server located at: http://www.ebi.ac.uk/msd-srv/msdsite.

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Section: Methodsmentioning

confidence: 99%

MSDsite: A database search and retrieval system for the analysis and viewing of bound ligands and active sites

et al. 2004

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“…At the same time, users' specification of the concept hierarchy is interpreted and translated into hierarchies defining the structure of the matrix. This concept hierarchy is very similar to the data warehousing (Hobbs, Hillson, Lawande, & Smith, 2005). This hierarchy allows users to roll up and drill down the data very easily and also introduce the dynamic to the matrix.…”

Section: System Architecturementioning

confidence: 99%

Distributed Intelligence for Constructing Economic Models

2010

International Journal of Organizational and Collective Intelligence

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This paper presents an integrated and distributed intelligent system being capable of automatically estimating and updating large-size economic models. The input-output model of economics uses a matrix representation of a nation’s (or a region’s) economy to predict the effect of changes in one industry on others and by consumers, government, and foreign suppliers on the economy (Miller & Blair, 1985). To construct the model reflecting the underlying industry structure faithfully, multiple sources of data are collected and integrated together. The system in this paper facilitates this estimation process by integrating a series of components with the purposes of data retrieval, data integration, machine learning, and quality checking. More importantly, the complexity of national economy leads to extremely large-size models to represent every detail of an economy, which requires the system to have the capacity for processing large amounts of data. This paper demonstrates that the major bottleneck is the memory allocation, and to include more memory, the machine learning component is built on a distributed platform and constructs the matrix by analyzing historical and spatial data simultaneously. This system is the first distributed matrix estimation package for such a large-size economic matrix.

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“…Computer technologies have garnered considerable attention due to their inherent advantages, including computational power, speed, and storage capacity. Researchers' focus has shifted to develop an automated method for cancer detection (prostate cancer [ 11 ], breast cancer [ 12 ], etc.) based on computer-aided diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Automated Detection and Characterization of Colon Cancer with Deep Convolutional Neural Networks

Hasan

Ali

Rahman

et al. 2022

Journal of Healthcare Engineering

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Colon cancer is a momentous reason for illness and death in people. The conclusive diagnosis of colon cancer is made through histological examination. Convolutional neural networks are being used to analyze colon cancer via digital image processing with the introduction of whole-slide imaging. Accurate categorization of colon cancers is necessary for capable analysis. Our objective is to promote a system for detecting and classifying colon adenocarcinomas by applying a deep convolutional neural network (DCNN) model with some preprocessing techniques on digital histopathology images. It is a leading cause of cancer-related death, despite the fact that both traditional and modern methods are capable of comparing images that may encompass cancer regions of various sorts after looking at a significant number of colon cancer images. The fundamental problem for colon histopathologists is differentiating benign from malignant illnesses to having some complicated factors. A cancer diagnosis can be automated through artificial intelligence (AI), enabling us to appraise more patients in less time and at a decreased cost. Modern deep learning (MDL) and digital image processing (DIP) approaches are used to accomplish this. The results indicate that the proposed structure can accurately analyze cancer tissues to a maximum of 99.80%. By implementing this approach, medical practitioners will establish an automated and reliable system for detecting various forms of colon cancer. Moreover, CAD systems will be built in the near future to extract numerous aspects from colonoscopic images for use as a preprocessing module for colon cancer diagnosis.

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Data Warehousing

Cited by 5 publications

References 0 publications

MSDsite: A database search and retrieval system for the analysis and viewing of bound ligands and active sites

MSDsite: A database search and retrieval system for the analysis and viewing of bound ligands and active sites

Distributed Intelligence for Constructing Economic Models

Automated Detection and Characterization of Colon Cancer with Deep Convolutional Neural Networks

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