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Data Science Cloud Service (

Data Science Cloud Service – DATAMIXI is a cognitive analysis service that discovers hidden patterns between data and predicts future trends. This is done by merging, analyzing deep data, and visualizing from various viewpoints, along with insights for intelligent data analysis and artificial intelligence. This is a data science consulting service designed for IT engineers and developers who want to integrate AI-based data analysis services into projects as a mashup.

< Data Science Cloud Service – DATAMIXI >

Data Science Cloud Service – DATAMIXI consists of data science, data curation, and cognitive analysis services. Data science is a cloud service that supports the entire process from establishing big data and analyzing to utilizing it. Data curation indicates a structure that enables the collaboration between a data builder and software system characterizing Saltlux’s data establishment and analysis processes. The cognitive analysis service includes trend analysis, emotion analysis, analysis, and visualization services based on a data set of tens of billions of unit instances.

Main features

DATAMIXI, the only data science portal for AI and data scientists in Korea, is capable of discovering hidden patterns between data and predicting future trends. This is done by merging, analyzing deep data, and visualizing from various viewpoints through cognitive analysis, combining insights for data analysis and artificial intelligence.

  •  The only domestic Big Data & AI community

This is the only Big Data and AI community with data scientists in Korea to help users communicate with experts and get the latest information. The best experts in each field, including data architects, data engineers, and data scientists are working with DATAMIXI.

  • Uses the largest domestic intelligent big data platform

You can use the largest domestic intelligent platforms combining the best solutions in each field, including collection, storage, data analysis, machine learning and reasoning.

  • Provides the world’s best data processing service

Provide the world’s best data service (Data as a Service) that enables the machine to read, learn and understand semantically through the big data collection engine ‘TORNADO’, with the support from the internal domestic and international professional curation center and AI research center.

  • Provides Big Data analysis technology through intelligent OpenAPI

Provide the best service based on Saltlux’s technologies with 20-year experience in the artificial intelligence field, from big data to large-scale machine learning and reasoning. You can use various analysis services integrating big data and AI technologies for tests and services.

  • Provides Asia’s largest data

You can receive or use big data that can be used immediately for the analysis of various dictionaries by domain in addition to social data, open data, linked data and real-time data

  • Provides free intelligent cognitive analysis service

Provide the AI cognitive analysis service with free social data of approximately 20 billion cases. It can discover hidden patterns between data and predict future trends by merging, analyzing deep data, and visualizing from various viewpoints. The premium personalized service of responding to customers’ requirements and providing results is also delivered as a complete personalized collection and analysis consulting service.

Main Services

Data Science Service

Saltlux’s Data Science Service is a consulting and educational service that provides practical IT knowledge and technology training for the whole service process. This ranges from the data collection and purification, successful cognitive analysis and machine learning accumulated over the past 20 years, machine learning and analysis models optimization, evaluation to prediction and intelligence result visualization.

< Data science service >

DATAMIXI’s Data Science Service integrates computer engineering, mathematical statistics, machine learning algorithms, and domain knowledge modeling methods through Saltlux’s unique dual spiral methodology. This eventually leads to the development of AI-based knowledge services, such as intelligent big data analysis services, Q&A, or dialog services.

< Dual spiral methodology-based data service >

Active cooperation between humans and machines (human-in-the-loop) is necessary for outstanding deep data analysis and intelligent services. DATAMIXI’s Data Science System is based on a dual spiral methodology where algorithms, tools, and experts actively cooperate.

For typical data science processes, the collection and purification of data by applying Saltlux’s unique dual spiral methodology, machine learning and analysis model selection and optimization, evaluation and visualization of predictions, and Intelligence results are carried out repeatedly.

< Data science consulting service process >

① Requirement analysis step

This is the step in which necessary data resources for data analysis are defined and the direction is identified through the deduction of analysis and intelligence goals, together with analysis and understanding of core problems in the data analysis required by a customer.

② Data curation step

The biggest difficulty in the execution of deep analysis and machine learning is the purification of big data, including any errors and lack of learning data. Data curation is the step to collect the data resources defined in the requirement step, purify data through processes, tools, and trained experts that meet each analysis and intelligence goal, and product data for analysis and learning through filtering.

③ Data analysis and learning step

This is the step to performing traditional statistical analysis and deep data analysis, using various machine learning technologies (CRF, SVM) and deep neural network-based deep learning technologies (CNN, RNN). During this process, large-scale data machine learning, prediction, and deep learning-based deep analysis are carried out, using the intelligent analysis platform combining Saltlux’s analysis engines and open sources (R, TensorFlow). Later, it produces an optimal analysis result that meets customers’ requirements through model verification, evaluation, tuning of model parameters, and changing the learning algorithms.

④ Data analysis verification and feedback step

In this step, knowledge, patterns, and exceptions are discovered from the analyzed results, or the learning and prediction analysis results are evaluated and verified through feedback from internal/external experts or customers before delivering the final results.

⑤ Final data analysis report step

In this final step, you receive the data analysis report that meets customers’ requirements, making data analysis and utilization a new competitive power for individuals and organizations.

Data curation service

It includes all activities to improve the data value, such as meta information tagging (annotation), classification, and learning data generation in data collection and purification. For deep analysis and machine learning, large-scale data must be secured and processed in a form that machines can read, learn, and understand semantically. With 20 years of experience in data quality management and machine learning, we are capable of delivering the world’s best data curation services.

< Data Curation Service >

① Data curation service process

Six data curation steps are applied commonly to all domains, and the expert teams in each step systematically cooperate in establishing a customer’s knowledge service.

② Data curation service function

Data curation embraces all activities that improve the value of data use. In addition to general data processing fields, such as data digitalization through books, raw data collection and data purification, the professional data curation services, such as image and video annotation, R&D data annotation, and establishment of the knowledge base, is provided as follows.

Intelligent Cognitive Analysis Service

Saltlux’s Intelligent Cognitive Analysis Service provides free advanced analysis through convergence analysis, related subject analysis, emotion analysis, trend analysis, issue detection, and real-time R-linkage using social data from more than 10 billion cases. It also comes with free intelligent cognitive analysis for semantic networks in data.

1) The data function allows users to directly upload, register and use their own data and public data from the data service.

2) The data merging function enables you to create data optimized for analysis by selecting and merging desired elements from two or more files.

3) The widget creating function allows you to apply various charts and create a widget through intelligent analysis for an analysis subject of interest with provided social data.

4) The dashboard creating function enables users to create a personalized dashboard by placing widgets in a specific location via the drag and drop method.

5) The web sharing and publishing function allows sharing of the dashboard created from various viewpoints via gallery or SNS.

① My Data function

The My Data function is a cognitive analysis service that enables processing, storing, and registering social data of 100+ cases provided by Saltlux, open data of 340,000 cases, or user data according to their needs in the form of CSV or Excel files.

② Analysis widget function

The analysis widget function carries out intelligent cognitive analysis using social data of 100+ cases provided by Saltlux, open data of 340,000 cases, or user data according to their needs. Users can apply analysis results to various charts to create a user analysis subject or user widget. This function can be divided into the cognitive analysis using social big data and the cognitive analysis using my data. You can also perform trend analysis, related keyword analysis, and emotion analysis using the detailed cognitive analysis function.

③ User dashboard and gallery function

You can store the user cognitive analysis result widget in the analysis widget gallery and create a dashboard from it. The created dashboard can be stored and registered in the user dashboard gallery. You can later share it with other users and download it through user selection.

Data processing and machine learning function service – Dataiku

As an intelligent centralized data-based big data platform, it uses the analysis function to ensure that the business maintains close ties to the company process, not just at the data storing step. It also supports the data modeling step through the machine learning process and applies that data to company operations.

< Data Science Cloud Service – Dataiku >

① Data search function

This function generates an automatic report for the data set and points out potential data quality problems. It also creates single data, statistics of multi-variables, and a detailed data set audit report. Users can filter and search for data as easily as Excel. Furthermore, you can gain insights by expanding the analysis range through execution in Spark, Hadoop, or SQL engines.

② Data pre-processing and visual conversion function

Users can easily access more than 80 embedded visual processors to prevent arguments about codeless data. It also allows the conversion of automatically suggested context and performs large-scale data work.

③ Machine learning function

This function allows automatic engineering, generation, and selection to use of all types of model data. The model hyperparameter is optimized using various cross-validation test strategies. Users can gain an immediate visual insight from the model (the importance of a variable, parameter interaction, or characteristics) and evaluate the model performance through detailed metrics.

④ Machine learning-based model distribution function

This function allows analysts and data scientists to place models in the production within a few clicks. Data cleaning, enrichment, and preprocessing become a score pipeline. As the versions of distributed models are managed, users can distribute a new version, compare, and roll back at any time.

⑤ Data creation information management function

The distribution model includes all steps necessary for data creation (① development of data creation model (workflow), ② model and production data test, ③ data prototype (verification before production), ④ data commercialization (data and creation model packaging) necessary for data production in a single UI).

Main competitive characteristics

  • Data analysis – Analysis visualization

Users can gain insights through author network analysis, similar papers, core technologies, related technologies and keywords, convergence analysis of different technologies, cognitive analysis, and deep analysis. It is also capable of determining competitors’ technologies and R&D status, briefing on government policies in R&D fields, new technology sensing, and trend monitoring in R&D fields.

  • Data curation – Conversion into smart data

Data curation includes all activities to improve the value of data utilization, such as annotation, classification, learning data creation in data collection, and purification. To perform data-based deep analysis and machine learning, it is crucial to secure and process large-scale data in a form that machines can read (readable), learn (learnable), and understand (understandable) semantically.

  • Provide the only data science platform service in Korea – Conversion into Science Total Service

It supports all tasks to gain insights or implement an intelligent system using data collection, curation, statistical analysis, and machine learning. Even those who have no experience in technology can also carry out data analysis with this product.

  • Machine learning, AI – prediction of research experiments and results

When internal research data and experiment data (graphs, tables, images, chemical formulas from outside papers) are prepared and curated (extracted, purified, and processed), users can quickly receive results by carrying out a research experiment indirectly through the ML function using that data.

  • Complete collection and integration of internal and external data – Data Banking

It enables the collecting, sharing, and reusing of internally scattered research data. Users can collect and internalize various unstructured data, such as papers, patents, and external technical documents. It embeds the largest collection functions (six types) among Korean and overseas collection engines and secures the best performance through real-time data collection and processing technology.

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