Automated Data Integration of Biodiversity with OLAP and OLTP

Biodiversity is one of emerging issue over decades; many have performed research to map and to document the data over the world. This issue is very important due to the event of extinction have been accelerating happening because of human extinction. Bengkulu, as one of the province lied in one of 19 hotspots in the world, Sundanese, has experienced the degradation of flora and fauna over the case of forest degradation and habitat loss. Although many application and software has been developed to solve the case, the existences of data standardization still become an issue over this problem. In this research, study of data integration had been developed to make the process of biodiversity data acquisition can be more effective and efficient. The system proposed the integration based on OLAP and OLTP that will be connected to IUCN, as one of the biggest center for monitoring the loss of biodiversity all around the world. This application had been built with web based using UML design and followed SDLC to provide the best fit of the need. This research had also succeeded to build the automated integration to show the record of dynamic number over biodiversity existences in Bengkulu. The application had been tested using black box and has the perfect performance (100%) over the testing that can help the monitoring process over biodiversity data.

Usage of the machine learning to organize time series and find anomalies

The subject of the study is the process of collecting, preparing, and searching for anomalies on data from heterogeneous sources. Economic information is naturally heterogeneous and semi-structured or unstructured. This makes pre-processing of input dynamic data an important prerequisite for the detection of significant patterns and knowledge in the subject area, so the topic of research is relevant. Pre-processing of data is several unique problems that have led to the emergence of various algorithms and heuristic methods for solving such pre-processing problems as merging and cleaning and identifying variables. In this work, an algorithm for preprocessing and searching for anomalies using LSTM is formulated, which allows you to consolidate into a single database and structure information by time series from different sources, as well as search for anomalies in an automated mode. A key modification of the preprocessing method proposed by the authors is the technology of automated data integration. The technology proposed by the authors involves the joint use of methods for building a fuzzy time series and machine lexical matching on a thesaurus network, as well as the use of a universal database built using the MIVAR concept. The preprocessing algorithm forms a single data model with the possibility of transforming the periodicity and semantics of the data set and integrating into a single information bank data that can come from various sources.

An Integrative Clinical Database and Diagnostics Platform for Biomarker Identification and Analysis in Ion Mobility Spectra of Human Exhaled Air

Summary Over the last decade the evaluation of odors and vapors in human breath has gained more and more attention, particularly in the diagnostics of pulmonary diseases. Ion mobility spectrometry coupled with multi-capillary columns (MCC/IMS), is a well known technology for detecting volatile organic compounds (VOCs) in air. It is a comparatively inexpensive, non-invasive, high-throughput method, which is able to handle the moisture that comes with human exhaled air, and allows for characterizing of VOCs in very low concentrations. To identify discriminating compounds as biomarkers, it is necessary to have a clear understanding of the detailed composition of human breath. Therefore, in addition to the clinical studies, there is a need for a flexible and comprehensive centralized data repository, which is capable of gathering all kinds of related information. Moreover, there is a demand for automated data integration and semi-automated data analysis, in particular with regard to the rapid data accumulation, emerging from the high-throughput nature of the MCC/IMS technology. Here, we present a comprehensive database application and analysis platform, which combines metabolic maps with heterogeneous biomedical data in a well-structured manner. The design of the database is based on a hybrid of the entity-attribute- value (EAV) model and the EAV-CR, which incorporates the concepts of classes and relationships. Additionally it offers an intuitive user interface that provides easy and quick access to the platform’s functionality: automated data integration and integrity validation, versioning and roll-back strategy, data retrieval as well as semi-automatic data mining and machine learning capabilities. The platform will support MCC/IMS-based biomarker identification and validation. The software, schemata, data sets and further information is publicly available at http://imsdb.mpi-inf.mpg.de.