Conference Paper

ICube: A similarity-based data cube for medical images

DOI: 10.1109/CBMS.2010.6042663 Conference: IEEE 23rd International Symposium on Computer-Based Medical Systems (CBMS 2010), Perth, Australia, October 12-15, 2010
Source: DBLP


Issuing analytical (OLAP) similarity queries based on images over data warehouses is a core problem in the medical field, as these queries can allow for the investigation and analysis of health data in decisionmaking processes. In this paper, we propose iCube, a similarity-based data cube for medical images. iCube is an extended data warehouse that encompasses a dimension table specifically designed to store intrinsic features of images, therefore allowing OLAP similarity queries over images. We also show how to build iCube and how to perform OLAP query processing over images. Comparisons of iCube with the current data warehousing technology aided by a metric access method showed that iCube provided an impre ssive performance improvement to process OLAP similarity queries over images. iCube performance gain ranged from 43% up to 76.7%.

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    • "Using data warehouse have been presented in following topics already : A water quality system[4], safety risks analysis in metro tunnels [5], a decision support system for sewer infrastructure management [6],Benchmarking in Clinical Rehabilitation [7] , health care systems [8],Translational Research [9] , Medical systems [10] [11], Merchandising System[12], education system[13], e-learning platform[14], and A metaphoric trajectory data warehouse for Olympic athlete follow‐up [15] and etc.[15-30], are the examples of designing and presenting data warehouse in diverse subjects. "
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    ABSTRACT: The Data Warehouse can be described as an integrated data source that is used to analyze huge amount of data. Data warehouses are used to process and aggregate data to survey and find out earlier unidentified prototypes , styles and relationships to produce information for superior decision making. Although databases can be used for this purpose, analyzing databases requires complex nested query processing which is inefficient on huge amount of data. One of the important business fields that contains huge databases is Tourism. In this paper, we propose a data warehouse for the flexible analysis of Tourism data that are collected from databases in travel agencies to help managers, governments, etc. to better decision making about tourism issues. The performance comparison between tourism database and tourism data warehouse analysis for a huge amount of data, show the advantages of the proposed analysis approach.
    Full-text · Conference Paper · Apr 2015
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    • "The authors proposed a clustering structure measure to explore images, dynamic aggregation selection to improve computations and a new type of OLAP operation to support overlapping. iCube [3] is a similarity-based data cube for medical images. It added a special dimension to store content-based features providing capabilities for OLAP similarity queries over images. "
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    ABSTRACT: Among the technologies involved on Business Intelligence, Data Warehouse and OLAP have been widely used to identify, collect, process, integrate and analyze information for decision making, thus promoting business management. Data stored in a data warehouse used by conventional OLAP systems are structured in nature. However, data such as text documents, images and videos, characterized as semi or unstructured data, may also contain information of great value to business. In this context, we applied the systematic review methodology with the purpose of identifying, extracting and summarizing the main research results focused on the integration of unstructured data in data warehouse environments. We raised forty two studies which were classified in order to identify ongoing research subjects and potential gaps as future trends.
    Full-text · Article · Jan 2014
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    ABSTRACT: Medical images are fundamental in medical processes particularly in the disease surveillance which is a practice that enables to monitor the evolution of patients' states. This practice cannot be understood and described only by a current image but requires the observation of image sequences in order to follow up the evolution of the disease from one human body location to another. This work aims to model a data warehouse where images and their related sequences are gathered and analyzed for decision making purposes such as disease evolution surveillance. The images' features are gathered as intrinsic features representing both the content-based and the description-based descriptors combined to the experts' annotations. We take into account the various modalities of images with the related temporal relationships which describe the sequence, and the conventional dimensions interfering for the target analysis.
    No preview · Conference Paper · Aug 2013