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MiningLamp Recognized by Leading Independent Research Firm Forrester, Evaluated Among Industry Leaders
  • USA - English


News provided by

MiningLamp Software

13 Dec, 2018, 16:06 IST

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BEIJING, Dec. 13, 2018 /PRNewswire/ -- Beijing MiningLamp Software System Co Ltd ("MiningLamp" for short) today announced that it has been recognized in the newly released Wave report from Forrester on the PAML (Predictive Analytics and Machine Learning Solutions) market in China.

Predictive Analytics and Machine Learning (PAML) is one of the key big data and AI market segments. Forrester, a world-renowned organization specializing in market research and consultation, carried out an assessment on the nine most significant PAML solution providers in the Chinese market.

THE FORRESTER WAVE Predictive Analytics And Machine Learning Solutions in China
THE FORRESTER WAVE Predictive Analytics And Machine Learning Solutions in China

The assessment, released as The Forrester Wave™: Predictive Analytics and Machine Learning Solutions in China, Q4 2018, was a 24-criteria evaluation of current offering, strategy, and market presence.

As Forrester stated in the report, MiningLamp's DataInsight, a visualized knowledge graph analysis platform, could easily build a machine learning model based on Spark. In cooperation with clients, MiningLamp doesn't just implement its PAML R&D strategies, but rather provides a vertical solution. For example, CONA supports automatic data governance and feature engineering and SCOPA offers a highly abstract and specialized PAML models for the sectors of public security, finance and manufacturing.

A public security knowledge graph initiated by MiningLamp has accumulated over 80% various data models. Based on a profound vertical knowledge graph-an essential tool for clients, MiningLamp has not only achieved vertical integration, but also begun to scale out in more mature cognitive intelligence sectors.

Since its establishment in 2014, MiningLamp has stayed committed to the R&D of cognitive technologies to map out an industrial knowledge graph for government agencies and enterprises in public security, digital city, industry engineering and finance. Now, MiningLamp has gradually linked perception and cognition in various application scenarios.

MiningLamp applied the knowledge graph in more than 30 provincial and municipal ministries of public security in China, safeguarding multiple domestic and international security events. In the financial sector, MiningLamp built the first banking knowledge graph nationwide and initiated an application system based on various business scenarios for benchmark banks such as People's Bank of China (PBC), China Everbright Bank (CEB), and Bank of Communications (BOCOM). As a result, MiningLamp helped enhance their risk management ability and risk control efficiency. For digital cities, MiningLamp helped clients build up China's first intelligent platform for managing the full life-cycle data of automobiles.

MiningLamp also helped improve work efficiency and ease security risks and operating costs for rail transit organizations, putting the transportation industry onto the path towards IT and intelligence application. In so doing, MiningLamp strives to combine industrial expertise with its one-stop big data service of "technology + business + standard" in public security, industry engineering, digital city, and finance, boosting an industrial upgrade from individual empowerment to full intelligence for enterprises.

As a pioneer in the era of big data + AI, MiningLamp knows well that every day, every second, new technologies and business models emerge. After 30 years of development for China's software, digital information technology is about to embrace another round of revolution. With that in mind, MiningLamp will stick to its values of "Ultimate Pursuit of Technology and Service", extending human intelligence with technology, and improving efficiency for all industries, thereby making its own contributions to promoting big data and AI in China.

Photo - https://photos.prnasia.com/prnh/20181213/2325925-1

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