database and machine learning


Machine Learning Database is an open-source database designed for machine learning. store data, explore it using SQL, then train machine learning models and expose them as APIs.

a relational database like Postgres, MySQL, Amazon Redshift or BigQuery will fit your needs. These structured, relational databases are great when you know exactly what kind of data you’re going to receive and how it links together — basically how rows and columns relate.

The open international soccer database for machine learning
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How well can machine learning predict the outcome of a soccer game, given the most commonly and freely available match data To help answer this question and to facilitate machine learning research in soccer, we have developed the Open International Soccer

Database dependency discovery: a machine learning approach
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Database dependencies, such as functional and multivalued dependencies, express the presence of structure in database relations, that can be utilised in the database design process. The discovery of database dependencies can be viewed as an induction problem

Automatic database management system tuning through large-scale machine learning
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Database management system (DBMS) configuration tuning is an essential aspect of any data-intensive application effort. But this is historically a difficult task because DBMSs have hundreds of configuration knobs that control everything in the system, such as the amount

The mnist database of handwritten digit images for machine learning research [best of the web]
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The MNIST database was constructed out of the original NIST database ; hence, modified NIST or MNIST. There are 60,000 training images (some of these training images can also be used for cross- validation purposes) and 000 test images, both drawn from the same distriWe developed DeepSolar, a deep learning framework analyzing satellite imagery to identify the GPS locations and sizes of solar photovoltaic panels. Leveraging its high accuracy and scalability, we constructed a comprehensive high-fidelity solar deployment database for theIn recent years the use of graph based representation has gained popularity in pattern recognition and machine learning . As a matter of fact, object representation by means of graphs has a number of advantages over feature vectors. Therefore, various algorithms for

Mlog: Towards declarative in- database machine learning
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We demonstrate MLog, a high-level language that integrates machine learning into data management systems. Unlike existing machine learning frameworks (eg, TensorFlow, Theano, and Caffe), MLog is declarative, in the sense that the system manages all data This study investigates the performance and robustness of regression machine learning models in the presence of variability in the experimental database . The main objective of this work is to predict the ultimate load of circular concrete-filled steel tubes. The simulationsSchema matching, the problem of finding mappings between the attributes of two semantically related database schemas, is an important aspect of many database applications such as schema integration, data warehousing, and electronic commerce

Dimension analysis of subjective thermal comfort metrics based on ASHRAE Global Thermal Comfort Database using machine learning
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We analyzed the ASHRAE Global Thermal Comfort Database II to answer a fundamental but overlooked question in thermal comfort studies: how many and which subjective metrics should be used for the assessment of the occupants thermal experience. We found that the

Database establishment for machine learning in nilm
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Nonintrusive load monitoring (NILM) is a problem of identifying operating appliances and estimating their energy consumptions based on whole home electric signals. Machine learning concepts and methods have been gradually applied to tackle NILM. A key factor of

Machine learning for potential energy surfaces: An extensive database and assessment of methods
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On the basis of a new extensive database constructed for the purpose, we assess various Machine Learning (ML) algorithms to predict energies in the framework of potential energy surface (PES) construction and discuss black box character, robustness, and efficiency. ThePredicting building occupants thermal comfort via machine learning (ML) is a hot research topic. Many algorithms and data processing methods have been applied to predict thermal comfort indices in different contexts. But few studies have systematically investigated how Background Traditional statistical approaches to prediction of outcomes have drawbacks when applied to large clinical databases. It is hypothesized that machine learning methodologies might overcome these limitations by considering higher-dimensional and

Database of two-dimensional hybrid perovskite materials: open-access collection of crystal structures, band gaps, and atomic partial charges predicted by machine
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We describe a first open-access database of experimentally investigated hybrid organic inorganic materials with a two-dimensional (2D) perovskite-like crystal structure. The database includes 515 compounds, containing 180 different organic cations, 10 metals (Pb

A novel fundus image reading tool for efficient generation of a multi-dimensional categorical image database for machine learning algorithm training
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Background: We described a novel multi-step retinal fundus image reading system for providing high-quality large data for machine learning algorithms, and assessed the grader variability in the large-scale dataset generated with this system. Methods: A 5-step retinal

Automatic pulmonary nodule detection applying deep learning or machine learning algorithms to the LIDC-IDRI database : a systematic review
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The aim of this study was to provide an overview of the literature available on machine learning (ML) algorithms applied to the Lung Image Database Consortium Image Collection (LIDC-IDRI) database as a tool for the optimization of detecting lung nodules in thoracic CT

Db4ml-an in-memory database kernel with machine learning support
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In this paper, we revisit the question of how ML algorithms can be best integrated into existing DBMSs to not only avoid expensive data copies to external ML tools but also to comply with regulatory reasons. The key observation is that database transactions already [HTML]

Patient journey through cases of depression from claims database using machine learning algorithms
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Health insurance and acute hospital-based claims have recently become available as real- world data after marketing in Japan and, thus, classification and prediction using the machine learning approach can be applied to them. However, the methodology used for the

Synergy of database techniques and machine learning models for string similarity search and join
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String data is ubiquitous and string similarity search and join are critical to the applications of information retrieval, data integration, data cleaning, and also big data analytics. To support these operations, many techniques in the database and machine learning areas have been

Machine learning and databases: The sound of things to come or a cacophony of hype
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General Terms Database Research, Machine Learning Keywords Database Research, Machine Learning Panel The last few years have seen increasing crossover between database research and machine learning . But is this crossover a wise choice for database research Electronic medical claims (EMCs) can be used to accurately predict the occurrence of a variety of diseases, which can contribute to precise medical interventions. While there is a growing interest in the application of machine learning (ML) techniques to address clinical The paper proposes a framework for deriving users profiles of typical behaviour and detecting atypical transactions which constitute fraudulent events or simply a change in users behaviour. The anomaly detection problem is presented and previous attempts to

Declarative recursive computation on an rdbms, or, why you should use a database for distributed machine learning
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A number of popular systems, most notably Googles TensorFlow, have been implemented from the ground up to support machine learning tasks. We consider how to make a very small set of changes to a modern relational database management system (RDBMS) to

Field trial of machine learningassisted and SDN-based optical network planning with network-scale monitoring database
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An SDN based network planning framework utilizing machine learning techniques and a network-scale monitoring database is implemented over an optical field-trial testbed comprised of 436.4 km fibre. Adaption of the spectral efficiency utilising probabilistic-shaping [HTML]

Exploring the clinical features of narcolepsy type 1 versus narcolepsy type 2 from European Narcolepsy Network database with machine learning
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Narcolepsy is a rare life-long disease that exists in two forms, narcolepsy type-1 (NT1) or type-2 (NT2), but only NT1 is accepted as clearly defined entity. Both types of narcolepsies belong to the group of central hypersomnias (CH), a spectrum of poorly defined diseases Objective We consider predictive models for clinical performance of pancreatic cancer patients based on machine learning techniques. The predictive performance of machine learning is compared with that of the linear and logistic regression techniques that dominate

Question answering using a large text database : A machine learning approach
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Creative Commons License ACL materials are Copyright 1963 ACL; other materials are copyrighted by their respective copyright holders. Materials prior to here are licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0

Retro: Relation retrofitting for in- database machine learning on textual data
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There are massive amounts of textual data residing in databases, valuable for many machine learning (ML) tasks. Since ML techniques depend on numerical input representations, word embeddings are increasingly utilized to convert symbolic [HTML]

Design and development of lubricating material database and research on performance prediction method of machine learning
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Long developing period and cumbersome evaluation for the lubricating materials performance seriously jeopardize the successful development and application of any database system in tribological field. Such major setback can be solved effectively byManual analysis of mass spectrometry data is a current bottleneck in high throughput proteomics. In particular, the need to manually validate the results of mass spectrometry database searching algorithms can be prohibitively time-consuming. Development of

Development of a global infectious disease activity database using natural language processing, machine learning and human expertise
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Objective We assessed whether machine learning can be utilized to allow efficient extraction of infectious disease activity information from online media reports. Materials and Methods We curated a data set of labeled media reports (n= 8322) indicating which articles contain [HTML]

Development of the arabic voice pathology database and its evaluation by using speech features and machine learning algorithms
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A voice disorder database is an essential element in doing research on automatic voice disorder detection and classification. Ethnicity affects the voice characteristics of a person, and so it is necessary to develop a database by collecting the voice samples of the targeted

Advancing the large-scale CCS database for metabolomics and lipidomics at the machine learning era
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Metabolomics and lipidomics aim to comprehensively measure the dynamic changes of all metabolites and lipids that are present in biological systems. The use of ion mobility mass spectrometry (IM MS) for metabolomics and lipidomics has facilitated the separation and the

Weka: A machine learning workbench
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Addison Wesley. RJ McQueen, DL Neal, R. Dewar, SR Garner and CG Nevill-Manning, The WEKA machine learning _._. workbench its application toa real world agricultural database Proc Canadian Machine Learning Workshop, Banff, Canada The routine scalp electroencephalogram (rsEEG) is the most common clinical neurophysiology procedure. The most important role of rsEEG is to detect evidence of epilepsy, in the form of epileptiform transients (ETs), also known as spike or sharp wave

Applying machine learning to an Alzheimers database
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This paper explores the application of Machine Learning (ML) methods for classifying dementia status to improve accuracy over current dementia screening tools: the Blessed Orientation, Memory, and Concentration Exam (BOMC), and the Functional Activities [HTML]

A database for using machine learning and data mining techniques for coronary artery disease diagnosis
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We present the coronary artery disease (CAD) database a comprehensive resource, comprising 126 papers and 68 datasets relevant to CAD diagnosis, extracted from the scientific literature from 1992 and 2018. These data were collected to help advance

potential differentially expressed miRNAs as diagnostic biomarkers for hepatocellular carcinoma based on machine learning in The Cancer Genome Atlas database
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The present study aimed to identify novel diagnostic differentially expressed microRNAs (miRNAs/miRs) in order to understand the molecular mechanisms underlying hepatocellular carcinoma. The expression data of miRNA and mRNA were downloaded for differentialRecent cancer genome studies on many human cancer types have relied on multiple molecular high-throughput technologies. Given the vast amount of data that has been generated, there are surprisingly few databases which facilitate access to these data and