Multidimensional Databases Research Articles

Conformations of the HIV-1 protease: A crystal structure data set analysis

The HIV protease is an important drug target for HIV/AIDS therapy, and its structure and function have been extensively investigated. This enzyme performs an essential role in viral maturation by processing specific cleavage sites in the Gag and Gag-Pol precursor polyproteins so as to release their mature forms. This 99 amino acid aspartic protease works as a homodimer, with the active site localized in a central cavity capped by two flexible flap regions. The dimer presents closed or open conformations, which are involved in the substrate binding and release. Here the results of the analysis of a HIV-1 protease data set containing 552 dimer structures are reported. Different dimensionality reduction methods have been used in order to get information from this multidimensional database. Most of the structures in the data set belong to two conformational clusters. An interesting observation that comes from the analysis of these data is that some protease sequences are localized preferentially in specific areas of the conformational landscape of this protein.

ODG: Omics database generator - a tool for generating, querying, and analyzing multi-omics comparative databases to facilitate biological understanding

BackgroundRapid generation of omics data in recent years have resulted in vast amounts of disconnected datasets without systemic integration and knowledge building, while individual groups have made customized, annotated datasets available on the web with few ways to link them to in-lab datasets. With so many research groups generating their own data, the ability to relate it to the larger genomic and comparative genomic context is becoming increasingly crucial to make full use of the data.ResultsThe Omics Database Generator (ODG) allows users to create customized databases that utilize published genomics data integrated with experimental data which can be queried using a flexible graph database. When provided with omics and experimental data, ODG will create a comparative, multi-dimensional graph database. ODG can import definitions and annotations from other sources such as InterProScan, the Gene Ontology, ENZYME, UniPathway, and others. This annotation data can be especially useful for studying new or understudied species for which transcripts have only been predicted, and rapidly give additional layers of annotation to predicted genes. In better studied species, ODG can perform syntenic annotation translations or rapidly identify characteristics of a set of genes or nucleotide locations, such as hits from an association study. ODG provides a web-based user-interface for configuring the data import and for querying the database. Queries can also be run from the command-line and the database can be queried directly through programming language hooks available for most languages. ODG supports most common genomic formats as well as generic, easy to use tab-separated value format for user-provided annotations.ConclusionsODG is a user-friendly database generation and query tool that adapts to the supplied data to produce a comparative genomic database or multi-layered annotation database. ODG provides rapid comparative genomic annotation and is therefore particularly useful for non-model or understudied species. For species for which more data are available, ODG can be used to conduct complex multi-omics, pattern-matching queries.

Automating statistical diagrammatic representations with data characterization

The search for an efficient method to enhance data cognition is especially important when managing data from multidimensional databases. Open data policies have dramatically increased not only the volume of data available to the public, but also the need to automate the translation of data into efficient graphical representations. Graphic automation involves producing an algorithm that necessarily contains inputs derived from the type of data. A set of rules are then applied to combine the input variables and produce a graphical representation. Automated systems, however, fail to provide an efficient graphical representation because they only consider either a one-dimensional characterization of variables, which leads to an overwhelmingly large number of available solutions, a compositional algebra that leads to a single solution, or requires the user to predetermine the graphical representation. Therefore, we propose a multidimensional characterization of statistical variables that when complemented with a catalog of graphical representations that match any single combination, presents the user with a more specific set of suitable graphical representations to choose from. Cognitive studies can then determine the most efficient perceptual procedures to further shorten the path to the most efficient graphical representations. The examples used herein are limited to graphical representations with three variables given that the number of combinations increases drastically as the number of selected variables increases.

OLAP and machine learning

The objective of this tutorial is to present an overview of machine learning (ML) methods. This paper outlines different types of ML as well as techniques for each kind. It covers popular applications for different types of ML. On-Line Analytic Processing (OLAP) enables users of multidimensional databases to create online comparative summaries of data. This paper goes over commercial OLAP software available as well as OLAP techniques such as “slice and dice” and “drill down and roll up.” It discusses various techniques and metrics used to evaluate how accurate a ML algorithm is.

Aerodynamic database error filtering via SVD-like methods

A method is presented to filter out errors in multidimensional databases. The method does not require any a priori information about the nature of the errors, which need not be small, neither random, nor exhibit zero mean. Instead, they are only required to be relatively uncorrelated to the clean information contained in the database. The method presented is based on an improved multidimensional extension by the authors (2016) [21] of a seminal gappy reconstruction method, due to Everson and Sirovich (1995) [18], who developed a two-dimensional method, based on SVD, able to reconstruct lost information at known database positions. The improved gappy reconstruction method is evolved in this paper as an error filtering method in two steps, since it is adapted to first (a) identify the error locations in the database and then (b) reconstruct the information in these locations by treating the associated data as gappy data. The resulting method filters out O(1) errors in an efficient fashion, for both random and systematic errors. Also, the method performs well both when errors are concentrated and when they are spread along the database. The method is illustrated and tested in several toy model and aerodynamic databases, obtained by discretizing a transcendental function and CFD-calculating the pressure on the surface of a wing for varying values of the angle of attack.

Artificial Intelligence approach for Classifying Molecular Dataset using Density based technique with appropriate Euclidean Distance measure

Data mining is the process of extracting hidden analytic information from large databases using multiple algorithms and techniques. Artificial Intelligence is concerned with improving algorithms by employing problem solving techniques used by human beings. In this paper, we implemented a methodology to classify a huge data set into clusters using a standard mathematical equation. A new clustering algorithm is proposed which involuntarily evolves the number of clusters as well as proper partitioning from a data set with artificial intelligence techniques. Here for the assignment of points to different clusters a standard distance calculation measure Euclidean distance formula is used which is intended to overcome point symmetry based clustering. An idea to implement this methodology on chemical/molecular data came from a software program Fast Rigid Exhaustive Docking which generates interactions of new molecules whose biological activity is unknown. The molecular data thus obtained as scores are tabulated as values. This is considered as raw data, which is further preprocessed and hence from one point to other point distance is calculated using Euclidean distance which forms a multi dimensional database with several combinations. Later on clusters are analyzed and pattern recognition is performed by using an algorithm to obtain results. Density based clustering technique is performed for better results on molecular database.In the field of chemistry there is much need of data mining for identifying the best molecule whose biological activity value is unknown. Using Artificial intelligent techniques we predicted the biological activity of a molecule when a dependent core training set is provided and leads to different case studies of test set molecules for which data mining techniques are utilized. As data mining is a wide research area that has many applications so new algorithms like this can create a boon in research areas and hence evaluating best results.

OLAP technology and Machine learning as the tools for validation of the Numerical Models of Convective Clouds

In the present work we use the technologies of machine learning and OLAP for more accurate forecasting of such phenomena as a thunderstorm, hail, heavy rain, using the numerical model of convective cloud. Three methods of machine learning: support vector machine, logistic regression and ridge regression are used for making the decision on whether or not a dangerous convective phenomenon occurs at present atmospheric conditions. The OLAP technology is used for development of the concept of multidimensional data base intended for distinguishing the types of the phenomena (thunderstorm, heavy rainfall and light rain). Previously developed complex information system is used for collecting the data about the state of the atmosphere and about the place and at the time when dangerous convective phenomena are recorded.

Prediction of aircraft performances based on data collected by air traffic control centers

Accurate prediction of aircraft position is becoming more and more important for the future of air traffic. Currently, the lack of information about flights prevents us to fulfill future demands for the needed accuracy in 4D trajectory prediction. Until we get the necessary information from aircraft and until new more accurate methods are implemented and used, we propose an alternative method for predicting aircraft performances using machine learning from historical data about past flights collected in a multidimensional database. In that way, we can improve existing applications by providing them better inputs for their trajectory calculations. Our method uses flight plan data to predict performance values, which are suited individually for each flight. The results show that based on recorded past aircraft performances and related flight data we can effectively predict performances for future flights based on how similar flights behaved in the past.

Construction and Realization for Multi-dimensional Database Based on RDBMS

Based on ArcSDE, this paper has proposed a new construction and management method for spatial database which has better performance than the traditional method. Up to now, there are four kinds of management about GIS database:whole file pattern, the combination pattern of file and DBMS, whole DBMS pattern and object oriented pattern. In fact, the two former ones is managed by separating spatial data and attribute data, which results into many problem: the low query efficiency, the higher management difficulty, the problem of data communion, etc. Object oriented pattern, however, still need further research. So, for a long space it is still need DBMS co-work to constructe spatial database.ArcSDE is a server software product of ESRI used to access massively large multiuser geographic databases stored in relational database management systems (RDBMS). Being different from other database product (Oracel Spatial), ArcSDE provides a suite of services that enhance data management performance, extend the range of data types that can be stored in a RDBMS, enable schema portability between RDBMSs, and offer configuration flexibility.

Mining Popular Patterns from Multidimensional Database

Objectives: To extract popular patterns from multidimensional database. Design an efficient algorithm to find frequency and maximum transaction length of a pattern for mining popular patterns from multidimensional database. Analysis: Earlier to mine required patterns from database Apriori algorithm is used. After the frequent patterns, they have been extended to a many interesting patterns. However, to mine required patterns from a multidimensional database FP-growth algorithm have been extensively in use. Here we have implemented pop-growth technique to mine popular patterns from multidimensional database based on their popularity values. Findings: In this paper, we studied about popular patterns which give the popularity of each item or events in the entire database. Whereas Apriori and FP-growth algorithm depends upon the support or frequency measure of a itemset. Therefore, to obtain required patterns using these techniques one to mine FP-growth tree recursively this involves in more time consumption. In this paper, we have implemented a mining technique, which is prominent for multi-dimensional popular patterns. It overcomes the limitations of existing mining techniques. It implements lazy pruning technique and exhibits downward closure property. Improvement: Till date, mining of popular patterns based on their popularity measure is implemented only on transactional database and incremental database. But we have implemented this technique on a dynamic multidimensional database in which popular patterns can be mined in two dimensions. It involves in two steps: 1. The Pop-tree structure, which catches the vital information for the mining process of popular patterns. 2. The Pop-tree development calculates for mining popular patterns.

Dysphagia Management and Research in an Acute-Care Military Treatment Facility: The Role of Applied Informatics.

This report describes the development and preliminary analysis of a database for traumatically injured military service members with dysphagia. A multidimensional database was developed to capture clinical variables related to swallowing. Data were derived from clinical records and instrumental swallow studies, and ranged from demographics, injury characteristics, swallowing biomechanics, medications, and standardized tools (e.g., Glasgow Coma Scale, Penetration-Aspiration Scale). Bayesian Belief Network modeling was used to analyze the data at intermediate points, guide data collection, and predict outcomes. Predictive models were validated with independent data via receiver operating characteristic curves. The first iteration of the model (n = 48) revealed variables that could be collapsed for the second model (n = 96). The ability to predict recovery from dysphagia improved from the second to third models (area under the curve = 0.68 to 0.86). The third model, based on 161 cases, revealed "initial diet restrictions" as first-degree, and "Glasgow Coma Scale, intubation history, and diet change" as second-degree associates for diet restrictions at discharge. This project demonstrates the potential for bioinformatics to advance understanding of dysphagia. This database in concert with Bayesian Belief Network modeling makes it possible to explore predictive relationships between injuries and swallowing function, individual variability in recovery, and appropriate treatment options.

A knowledge discovery approach to urban analysis: Beyoglu Preservation Area as a data mine

Enhancing our knowledge of the complexities of cities in order to empower ourselves to make more informed decisions has always been a challenge for urban research. Recent developments in large-scale computing, together with the new techniques and automated tools for data collection and analysis are opening up promising opportunities for addressing this problem. The main motivation that served as the driving force behind this research is how these developments may contribute to urban data analysis. On this basis, the thesis focuses on urban data analysis in order to search for findings that can enhance our knowledge of urban environments, using the generic process of knowledge discovery using data mining. A knowledge discovery process based on data mining is a fully automated or semi-automated process which involves the application of computational tools and techniques to explore the “previously unknown, and potentially useful information” (Witten a Frank, 2005) hidden in large and often complex and multi-dimensional databases. This information can be obtained in the form of correlations amongst variables, data groupings (classes and clusters) or more complex hypotheses (probabilistic rules of co-occurrence, performance vectors of prediction models etc.). This research targets researchers and practitioners working in the field of urban studies who are interested in quantitative/computational approaches to urban data analysis and specifically aims to engage the interest of architects, urban designers and planners who do not have a background in statistics or in using data mining methods in their work.

Cost effective storage space for data cubes

View materialization is one of the most important techniques applied in multidimensional databases. The problem of selecting a set of views for materialization that minimizes queries response time under storage space constraint received significant attention over last twenty years. Many researchers concentrate on designing better view selection methods with respect to the running time or the cost of the solution. This paper summarizes our research on the problem of how much space should be allocated for views materialization to ensure good queries performance. In order to comprehensively investigate the problem and minimize the influence of untypical cases, the experiments described in this paper were done on the large data set, including large data cubes, rarely considered in previous papers. In particular, the relation between the number of data cube views and the space limit expressed as a percentage of the fully materialized data cube size and a multiple of the base view size is analysed. According to our experimental results, the allocation of large space for views materialization is not cost effective.

Extract transformation loading from OLTP to OLAP data using pentaho data integration

The design of the data warehouse in this case is expected to solve the problem of evaluation of learning results as well as the relevance of the information received to support decision-making by the leader. Data warehouse design is very important, which is designed to utilize the existing resources of information. GPA (Grade Point Average) data warehouse can be used for the process of evaluation, decision making and even further planning of the study program of PTIK. The diversity of data sources in the course PTIK make decisionmaking and evaluation process becomes not easier. Pentaho Data Integration is used to integrate data in PTIK easy. CPI data warehouse design with multidimensional database modeling approach using the dimension tables and fact tables.

An Efficient Approach for Processing Skyline Queries in Incomplete Multidimensional Database

In recent years, there has been great attention given to skyline queries that incorporate and provide more flexible query operators that return data items (skylines) which are not being dominated by other data items in all dimensions (attributes) of the database. Many variations in skyline techniques have been proposed in the literature. However, most of these techniques determine skylines by assuming that the values of all dimensions for every data item are available (complete). But this assumption is not always true particularly for large multidimensional database as some values may be missing (not applicable during the computation). In this paper, we proposed an efficient approach for processing skyline queries in incomplete database. The experimental results show that our proposed approach has significantly reduced the number of pairwise comparisons and the processing time in determining the skylines compared to the previous approaches.

Aerodynamic database reconstruction via gappy high order singular value decomposition

A method based on an iterative application of high order singular value decomposition is derived for the reconstruction of missing data in multidimensional databases. The method is inspired by a seminal gappy reconstruction method for two-dimensional databases invented by Everson and Sirovich (1995) [20] and improved by Beckers and Rixen (2003) [21] and Venturi and Karniadakis (2004) [22]. In addition, the method is adapted to treat both noisy and structured-but-non-rectangular databases.The method is calibrated and illustrated using a three-dimensional toy model database that is obtained by discretizing a transcendental function. The performance of the method is tested on three aerodynamic databases for the flow past a wing, one obtained by a semi-analytical method, and two resulting from computational fluid dynamics.

A knowledge discovery approach to urban analysis: Beyoglu Preservation Area as a data mine

A knowledge discovery approach to urban analysis: Beyoglu Preservation Area as a data mine

A knowledge discovery approach to urban analysis: Beyoglu Preservation Area as a data mine

A knowledge discovery approach to urban analysis: Beyoglu Preservation Area as a data mine

Pattern Recognition Approach in Multidimensional Databases: Application to the Global Terrorism Database

This paper presents a pattern recognition approach in multidimensional databases. The approach is based on a clustering method using the distance measurement between a reference profile and the database observations. Two distance measurements will be proposed: an adaptation of the Khi2 formula to the multidimensional context, extracted from the Multiple Correspondence Analysis (MCA), and the Euclidean distance. A comparison between the two distances will be provided to retain the most efficient one for the multidimensional clustering context. The proposed approach will be applied to a real case study representing armed attacks worldwide stored in the Global Terrorism Database (GTD).

Digital development: a database of cell lineage differentiation in C. elegans with lineage phenotypes, cell-specific gene functions and a multiscale model.

Developmental systems biology is poised to exploit large-scale data from two approaches: genomics and live imaging. The combination of the two offers the opportunity to map gene functions and gene networks in vivo at single-cell resolution using cell tracking and quantification of cellular phenotypes. Here we present Digital Development (http://www.digital-development.org), a database of cell lineage differentiation with curated phenotypes, cell-specific gene functions and a multiscale model. The database stores data from recent systematic studies of cell lineage differentiation in the C. elegans embryo containing ∼200 conserved genes, 1400 perturbed cell lineages and 600 000 digitized single cells. Users can conveniently browse, search and download four categories of phenotypic and functional information from an intuitive web interface. This information includes lineage differentiation phenotypes, cell-specific gene functions, differentiation landscapes and fate choices, and a multiscale model of lineage differentiation. Digital Development provides a comprehensive, curated, multidimensional database for developmental biology. The scale, resolution and richness of biological information presented here facilitate exploration of gene-specific and systems-level mechanisms of lineage differentiation in Metazoans.