预测分析软件被用在许多应用程序上以发现数据中未知的、隐藏的模式。
Predictive analytics software is used in many applications to discover unknown, hidden patterns in data.
不同分析产品的一些价值在于它们处理大量非结构化数据以发现隐藏含义的能力。
Some of the value of different analytics products is their ability to process large amounts of unstructured data to discover the latent meaning.
通过使用这个功能，组织现在可以直接对XML使用BI工具，发现以前隐藏在XML数据中的业务信息。
By taking advantage of this capability, organizations can now use their bi tools directly against XML to discover business insight previously locked in their XML data.
例如，如果在数据配置期间发现有隐藏的属性嵌入在文本字符串中，就可以修改规范化数据模型，把它们变成显式的属性。
For example, if hidden attributes are discovered to be embedded in a text string during data profiling, then these can be made explicit in a revised canonical data model.
例如，通过在主视图中不包含数据来隐藏数据(尽管从开发角度看很容易实现)并不能阻止决心找到该数据的人发现它。
For example, hiding data by not including it in the main views — although easy to accomplish from a development perspective — does not protect that data from someone who is determined to find it.
有许多新的商务见解来自于“死数据”：通过查看存储下来的以往的交易信息可以发现隐藏的相互关系。
Many new business insights come from "dead data" : stored information about past transactions that are examined to reveal hidden correlations.
Exeros技术可以自动地发现数据库之间隐藏的关系，帮助用户发现不同数据源的意义，避免手工数据映射过程。
Exeros technology automatically uncovers hidden relationships between databases, helping users make sense of disparate sources without the labor-intensive process of manually mapping data.
数据挖掘技术是对大量数据进行分析，发现数据中隐藏知识的一种技术。
Date mining is a technology that it can find hidden knowledge in a large amount of data.
不但显著提高了非线性生物模型回归参数的精度，而且还能发现原始数据中隐藏的问题。
It can not only improve regression precision of the nonlinear biology models but also can find problems hidden in the original data.
它可以发现一些隐藏在大量数据背后潜在的信息来预测事物发展趋势。
It can find the potential knowledge which hides behind the large data to forecast the trend of things development.
如何从浩如烟海的数据中发现隐藏的有用知识，创造更大的效益是一个迫切需要研究的课题。
How to discover quickly and exactly the useful information and knowledge and how to get more benefits, have become a key research topic.
并利用数据挖掘工具，发现隐藏在庞杂信息源中的知识和规律，为目标市场的分析与选择提供有价值的参考信息。
The knowledge and rule discovered by data mining tools are useful information for the analysis and selection of Objective market.
而概念格正是从数据中进行概念发现的有力工具，用来发现数据中隐藏的知识模式。
Concept lattice is a powerful tool for concept discovery from data, used to extract hidden knowledge pattern in data.
数据挖掘是信息提取的活动，其目标是发现隐藏的事实数据库。
Data mining is an information extraction activity whose goal is to discover hidden facts contained in databases.
金融数据的随机特性使得隐藏在数据中的内在规则难以被发现。
Financial data with random characteristics make it difficult to find out the rule hidden in data.
由于网络数据流量的急剧膨胀，人们迫切希望对流量数据进行更深层次的分析，以发现隐藏在数据中的知识。
Since the rapid growth of net flow, for discovering the knowledge behind the data, that the requirement of applying deeper analyses to this kind of data becomes more and more urgent.
CRM利用数据挖掘技术发现客户数据背后隐藏的、有用的、未曾预料的知识。
CRM can use data mining technology to find useful and unknown knowledge, and classify customers by using a clustering tool.
数据挖掘主要是用来找出隐藏在数据库当中那些有用的而未被发现的知识。
Data mining is the discovery of useful and potential knowledge hiding in databases.
通过试验和分析发现该方案具有安全性好、隐藏数据量大、速度快不易被检测等优点。
Through the experiment and analysis, this scheme has high security, big, quick and difficult to detect.
数据挖掘能发现数据之间隐藏的联系。
所谓数据挖掘技术就是通过对现实问题进行有效的模式提取，从大量的数据中发现隐藏于其后的规律或数据间的关系，从而分析、提取有用的知识，服务于管理决策。
Data mining picks up the practical issue with effective mode, finds the hiding rule from the abundant data, analyses and gets useful knowledge, serves for the decision-making.
同时，随着大量数据不断地收集和存储，隐藏在数据项集之间有价值的规则就越难发现。
As massive amount of data continuously being collected and stored, knowledge or regular hidden in large data sets are more difficult to find.
危化品事故中隐藏着真实的客观规律，但需要通过大量的数据分析才能发现。
Objective laws are hidden in incidents of hazardous chemicals, but they can be found only through the analysis large amounts of data.
聚类分析是数据挖掘中的一项重要技术，通过聚类可以发现隐藏在海量数据背后的知识。
Clustering analysis is an important research field of data mining, through which we can find hidden knowledge behind mass data.
它的最终目的就是发现隐藏在数据内部的规律和数据之间的特征，从而服务于管理和决策。
His ultimate purpose is to discover the characteristic concealing between internal law and data, thereby serving the management and decision-making.
数据挖掘可以称为数据库中的知识发现，它是从大量数据中发现并提取隐藏在其中的可信的、新颖的、有效的并能被人理解的模式的高级处理过程。
The data mining is also called the knowledge discovery in database, which discovers from large quantity of data and find authentic, novel and effective model that can be comprehended by people.
数据挖掘的研究目的是在大型数据集中发现那些隐藏的、人们感兴趣的具有特定规律的信息。
The research purpose of data mining is that in large scale data concentrated, find the certain regularity information that hidden, people are interested in.
如何发现在大型空间数据库中所隐藏的、预先未知的信息以辅助相应的应用，这就是目前空间数据挖掘的任务。
The main task of spatial data mining is to discovery the implicit, previous unknown, and potential useful information from these data.
如何发现在大型空间数据库中所隐藏的、预先未知的信息以辅助相应的应用，这就是目前空间数据挖掘的任务。
The main task of spatial data mining is to discovery the implicit, previous unknown, and potential useful information from these data.
应用推荐