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%.
"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  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. "
[Show abstract][Hide abstract] 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.
[Show abstract][Hide abstract] 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.
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining; 08/2013
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