When we talk about enterprise search, you have hundreds of users using your enterprise search software every day. The search logs of all these searches can hold quite a lot of valuable information about your users, their priority, what they are looking for, and more.
Enterprise search has come a long way today with the introduction of a handful of breakthrough concepts that have redefined the way users interact with search engines. Today, search engines are better equipped to provide results that are a close match to what the users seek.
Artificial intelligence (AI) has emerged as a key technology concept that is having its effect in every other domain and industry. It is the technology that enables machines to think, reason and even take decisions like humans do. As interesting as it may sound, AI holds a world of possibilities and has the potential to change the way we look at and interact with machines.
90% of the world's data has been created in the last two years alone. This statistic by Forbes shows that data is growing at a faster pace than ever, so much so that Big Data has already emerged as a major challenge and enterprises all across the globe are on the lookout for the most optimum ways to manage and analyze Big Data.
What is contextual search? Contextual search is defined as a search technology that focuses on the context of the query as well as the intent of the user in order to fetch the most relevant set of results.
We all are very well aware of the concept of Semantic Search and how it improves the accuracy and relevancy of search by understanding the search context and the user intent. The important definitions here are Context and Intent.
Enterprise search has come a long way in the last decade with the introduction of several advanced technologies. Today, the new age enterprise search tools are well-equipped to analyze even the most complex organizational data.
Enterprise search has come a long way and the enterprise search you have today is a far cry from the enterprise search that came into being in the early '90s. Today, there are a host of technologies that have made their advent into the world of enterprise search, only to make it better and more effective.
Well, let me start off with saying Healthcare is no stranger to changes. Challenges have always been there along the way and are constant, but so too have been the solutions. Healthcare is turning into a consumer driven industry, and providers are not only competing to create the most attractive, intuitive and impactful experience for the people around.
The exponential growth of unstructured data is a reality and enterprises are facing a growing challenge of leveraging this unsurmountable amount of unstructured data for insights to drive business growth.
According to a study by the International Data Group (IDG), unstructured data is growing at an alarming rate of 62% per year. The same study also suggests that by 2022, close to 93% of all data in the digital world will be unstructured!!
The biggest challenge enterprises face when using a keyword based search platform is that a major portion of organizational data comprises of unstructured data. According to the Market Pulse Survey by SailPoint, over 71% of enterprises across the globe are not sure about how to manage unstructured data and the likely reason is that unstructured data is difficult to comprehend for a keyword based search tool.
Given the exponential growth in medical literature, finding relevant information sooner is critical. Researchers, with more content and less time to analyse it, need systems that are smart and intelligent enough to integrate the scattered content, provide quicker discovery of information and tools for thorough analysis of content.
We know that computers understand programming languages but how about making them understand human language, the language that you and me speak? Natural Language Processing (NLP) is a field of study that makes this possible, as it focuses on enabling computers to analyse, understand and derive meaning from human language in order to perform a large number of tasks.
When enterprise searches are built from scratch, evaluation of the search quality remains key challenges of organizations implementing it. It always gives a feel of living in the darkness all the time. Such implementations demand enormous efforts and time. The chart below demonstrates a typical challenging situation in which organizations invest and work consistently on maturing the quality of searches over time, and yet remain far from satisfaction.
This article explains how to implement SOLR "document level security" using Manifold Connector Framework. ManifoldCF is an open source framework for pulling content out of a repository and sending it on to targets such as SOLR via a plug-in style and connector-based architecture.
According to the docker's website, "Docker is an open platform for developers and sysadmins to build, ship, and run distributed applications."
In simple words, it's one of the methods to run and deploy your software application. Docker allows you to create lightweight "virtual machines". Here lightweight virtual machines are nothing but docker containers.
Tesseract is probably the most accurate open source OCR engine available and with Apache Tika 1.7 you can now use the awesome Tesseract OCR parser within Tika!
Solr 5.x has support for Tika 1.7 (See this) . I wanted to try this in Solr 5.2 so I configured this on my machine, Below are the steps required to make TikaOCR work with Solr 5.2.
One of the lesser known but cool features of ReplicationHandler is support for index backup. You must have used ReplicationHandler in your project for replicating index from master to slave instances. if you want to take backup of index, you can do it as follows:
An ontology formally defines a common set of terms that are used to describe and represent a domain. An ontology is domain specific, and it is used to describe and represent an area of knowledge. It contains terms and the relationships among these terms. There is another level of relationship expressed by using a special group of terms: properties.
If you have multiple clients updating documents, it's really critical to ensure that newer version of the document is never overwritten by the older version. To address this problem, what you need is concurrency control, which is the process of managing simultaneous update of documents.