Data is everywhere. News, blog posts, emails, videos and chats are just a few examples of the multiple streams of data we encounter on a daily basis. The majority of these streams contain textual data – written language – containing countless facts, observations, perspectives and insights that could make or break your business.
It is often useful to relate a piece of text with the sentiment expressed in it. Extracting and processing sentiments from text provides not only a new emotional access pattern to your corpus but also new knowledge which can reveal new insights. Suppose you want to build a recommendation engine which leverages reviews to spot detailed strengths and weaknesses of different hotels, such as good location but bad staff. Or, it certainly makes a difference whether an article talks about your organization in a positive or negative manner.
One of the key components of Information Extraction (IE) and Knowledge Discovery (KD) is Named Entity Recognition, which is a machine learning technique that provides us with generalization capabilities based on lexical and contextual information. Named Entities are specific language elements that belong to certain predefined categories, such as persons names, locations, organizations, chemical elements or names of space missions. They are not easy to find and subsequently classify (for example, organizations and space missions share similar formatting and sometimes even context), but having them is of significant help for various tasks: improving search capabilities relating documents among themselves or...
Representation is one of the most complex and compelling tasks in machine learning. The way in which we represent facts, events, objects, labels, etc. affects how an autonomous learning agent can analyze them and extract insights, make predictions and deliver knowledge.
IntroductionIn the bucket filling problem you are given two empty buckets, each of a certain capacity, and a large supply of water. By filling, emptying and transferring water between the two buckets, you must try to end up with a situation where one of the buckets contains a required volume of water, or where both buckets together contain the required volume.
The GraphAware Audit Module seamlessly and transparently captures the full audit history who, when, and how a graph was modified.
Enterprise IT requirements are demanding and solutions are expected to be reliable, scalable, and continuously available. Databases accomplish this through clustering, the ability of several instances to connect and conceptually appear and operate as a single unit.
Spring and Spring Boot have become the Swiss Army knife of Java software development, offering dozens of useful modules across a wide range of concerns.One such module is Spring Boot Actuator, a sub-project of Spring Boot, that offers built-in, production-grade functionality to help monitor and interact with an application. Numerous endpoints are included that provide a wealth of information that, among others, include auditing, configuration, environment, and health details.
We are becoming increasingly dependent on technology. Yet, without diligent attention paid to cybersecurity, technology is vulnerable to unauthorized access, change or even destruction. These vulnerabilities pose threats to our individual and collective safety, security and human and economic well-being.Cybersecurity is therefore a vitally important global issue with substantial consequences that depends on safe, stable, and resilient security of our data, devices, and systems.
The success of many enterprises greatly depends on their ability to gather useful information and process it in a timely manner. Automation is essential and so is presentation, giving tangible feedback, to decision makers. This is where technology reaches out to management, where science and design are combined to put the right people in the position of making better and more sustainable choices.