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首页> 外文期刊>International journal of design & nature and ecodynamics >MORIARTY: IMPROVING 'TIME TO MARKET' IN BIG DATA AND ARTIFICIAL INTELLIGENCE APPLICATIONS
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MORIARTY: IMPROVING 'TIME TO MARKET' IN BIG DATA AND ARTIFICIAL INTELLIGENCE APPLICATIONS

机译：道德：改善大数据和人工智能应用程序中的“上市时间”

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摘要

The objective of this paper is to present the Moriarty framework and show one use case of the recommendation of entertainment events. Moriarty is a tool that can generate Big Data near real-time analytics solutions (Streaming Analytics). This new tool makes possible the collaboration among the data scientist and the software engineer. Through Moriarty, they join forces for the rapid generation of new software solutions. The data scientist works with algorithms and data transformations using a visual interface, while the software engineer works with the idea of services to be invoked. The underlying idea is that a user can build projects of Artificial Intelligence and Data Analytics without having to make any line of code. The main power of the tool is to reduce the 'time to market' in an application which embeds complex algorithms of Artificial Intelligence. It is based on different Artificial Intelligence algorithms (like Deep Learning, Natural Language Processing and Semantic Web) and Big Datamodules (Spark as a distributed data engine and access to NoSQL databases). Moriarty is divided into several layers; its core is a BPMN engine, which executes the processing and defines data analytics process, called workflows. Each workflow is defined by the standard BPMN model and is linked to a set of reusable functions or Artificial Intelligence algorithms written following a service-oriented architecture. An example of service presented is a recommendation application of restaurants, concerts, entertainment and events in general, where information is collected from social networks and websites, is processed by Natural Language Processingalgorithms and finally introduced into a graph database.
机译：本文的目的是提出Moriarty框架并显示娱乐事件推荐的一个用例。 Moriarty是一种可以在实时分析解决方案（Streaming Analytics）附近生成大数据的工具。这种新工具使数据科学家和软件工程师之间的协作成为可能。通过Moriarty，他们携手合作，快速生成新的软件解决方案。数据科学家使用可视界面处理算法和数据转换，而软件工程师则处理要调用的服务的想法。其基本思想是，用户无需编写任何代码即可构建人工智能和数据分析项目。该工具的主要功能是在嵌入了复杂人工智能算法的应用程序中减少“上市时间”。它基于不同的人工智能算法（如深度学习，自然语言处理和语义网）和大数据模块（作为分布式数据引擎的火花（Spark）和对NoSQL数据库的访问）。莫里亚蒂分为几层；它的核心是BPMN引擎，该引擎执行处理并定义数据分析过程，称为工作流。每个工作流程均由标准BPMN模型定义，并链接到遵循面向服务的体系结构编写的一组可重用功能或人工智能算法。所提供服务的一个示例是一般对餐馆，音乐会，娱乐场所和活动的推荐应用，其中从社交网络和网站收集信息，然后通过自然语言处理算法进行处理，最后将其引入图形数据库。

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