“Lateral thinking” was a big topic back in 2004 when I was in the Network Operations Center (NOC) business; one definition is: “(lateral thinking) is the solving of problems by an indirect and creative approach, typically through viewing the issue in a new and unusual light.” If it works, don’t touch it But the world of NOC operations, and generally IT Operations was anything but creative, not because we didn’t appreciate innovation per se, but because we valued reliability, consistency an uptime above all things, and those outcomes are the result of a long tradition in IT of approaching change,...
Do you think there is no space for a graph database in your company? Or it would be a huge effort to integrate a graph database into your product? I have to tell you: You can use a graph database like Neo4j without touching your product, and you can use it for managing your company’s knowledge as well as to improve your software development process. So, even if your business problem is not inherently graphy (hard to believe in 2018), there are a few reasons why you should think about your environment as a graph. Without knowing your core business,...
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. The data, in its native form, is completely useless because it doesn’t provide any value. It is sparse, distributed and unstructured – it is chaotic. To make sense of the data, we have to transform and organize it – a process that produces information. However, for the...
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. What’s out there? Extracting sentiments from texts is a difficult task. Many...
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. In this blog post, we focus on natural language processing and how to represent textual entities such as words, sentences, paragraphs or whole documents. Dealing with text is hard because, from the “algorithm” perspective, it is just a list of strings or characters. If we want to efficiently grab text and use it in various analytics tasks, we need...
“Relevance is the practice of improving search results for users by satisfying their information needs in the context of a particular user experience, while balancing how ranking impacts business’s needs.” [1] Providing relevant information to the user performing search queries or navigating a site is always a complex task. It requires a huge set of data, a process of progressive improvements, and self-tuning parameters together with infrastructure that can support them. Such search infrastructure must be introduced seamlessly and smoothly into the existing platform, with access to all relevant data flows to provide always up-to-date data. Moreover, it should allow...