What is Search Analytics?

The technique used to analyze search logs, which include large amounts of search data, is known as search analytics.

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What is Data Integration Platform? IT workers may use a data integration platform to combine data from numerous sources and create a full, accurate, and up-to-date dataset for BI, data analysis, and other applications and business processes.

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Text mining is something that every company needs today, especially at an era when companies are trying to manage massive volumes of data.

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Nowadays search engine technology has remarkably evolved. Since 2010 a lot of things have reformed as now identifying keywords is no longer enough and the focus has shifted...

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In the current scenario, when businesses are struggling to make sense of massive amounts of data, an advanced text analysis engine that makes it simple for businesses to analyse the data and generate insights from it is critical.

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In today's highly competitive world, businesses can't afford to miss out on using data to develop critical insights that drive corporate success.

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The objective of the new age enterprise search platform is to assist businesses in gaining hidden insights from the vast volumes of unstructured enterprise data that are constantly growing.

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Today AI is a significant advanced technological concept with ramifications across all domains and industries. It's the technology that allows machines to think, reason, and make decisions in the same way that people do.

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Unstructured data is difficult to grasp for a keyword-based search tool, which is most certainly the case.

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Semantic search is all about going a step further from a query's dictionary definition to understand the searcher's intent in a given environment.

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When we talk about enterprise search, what really defines its effectiveness is how much it helps meet the requirements of the users.

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When we say "Intelligent Search", we refer to a search experience that goes beyond the capability of keyword based traditional search and has a high level of understanding of what the user is looking for.

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Today computers can understand programming languages, but what about teaching them how to understand human language, the language that you and I speak?

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Data in the enterprise is unstructured and complex. Since typical keyword-based methods cannot be used to investigate this data, it must be analyzed utilizing the most modern data mining and text analysis engines available.

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An ontology can show the qualities of a subject area and how they are related by defining a set of ideas and categories that reflect the subject.

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What is the definition of content classification? The process of classifying content into preset categories is known as content classification.

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Unstructured enterprise data is extremely different and far more complex than the data you see on the internet every day.

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Every business relies on the ability to find the correct information precisely and promptly. In the enterprise, a lack of good search solutions has resulted in poor decision making, information fragmentation, and reinventions.

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The technology that analyses the actual sentiment behind a text or piece of material is known as sentiment analysis.

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Information overload occurs when the amount of input to a system exceeds its processing capacity. Decision makers have fairly limited cognitive processing capacity.

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Enterprises today are making efforts to leverage the power of data to stay ahead of the competition.

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Did you know that more than 80% of enterprise data consists of unstructured data? This data by Statista showcases the complexity of text analysis when it comes to enterprise data.

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What is the definition of data integration? Data integration is a method of data preparation that combines data from several sources and presents it to users in a logical manner.

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What is open source? It is defined as software released under a license that implies the owner has given the rights to use, analyze, modify and distribute the software as well as its source code to anybody and for any purpose.

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Enterprise search needs to be more effective today than ever as we are living in an era where enterprise data is not just increasing in volume but also getting more complex than ever.

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What is data integration/content integration and what is its significance in data mining? Content integration is a data preparation approach that brings together data from several sources and presents it to users in a cohesive manner.

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When you think about the new age search engines, what you find really amazing is the level of accuracy that these platforms achieve.

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Because enterprise data is complex and unstructured, analysing it is one of the most challenging challenges facing businesses today.

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You'll agree that the first name that springs to mind when thinking about searching online for your assignments and projects is a search engine like Yahoo or Google.

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End users must make complex database search requests to retrieve information due to the huge increase in the use of knowledge discovery apps.

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With the enormously large volumes of corporate information, it's natural to want to organize it. This makes it simple to find it in a content management system or on a website.

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Named Entity Recognition (NER) is a form of data extraction that is about finding and classifying named entities in unstructured data into pre-defined classes known as named entities.

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Natural Language Processing (NLP) has come a long way over the years, and it is now widely regarded as one of the most important technologies for revolutionizing search.

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The capacity of algorithms to interpret text has greatly increased as a result of recent developments in deep learning. Advanced artificial intelligence algorithms used creatively can be a valuable tool for conducting in-depth research.

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Enterprise data often involves large volumes of data that is ever on the increase as more and more information keeps getting added from a plethora of different sources.

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Text analysis, also known as text mining, is the act of analyzing large amounts of unstructured data to uncover previously unknown information and insights that can be utilized to make better decisions, along with other significant applications.

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Data is increasingly becoming the most valuable asset for organizations of all sizes. This has prompted businesses all over the world to seriously consider investing in technology

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How significant is enterprise search and do businesses really need to invest in an advanced new age enterprise search tool? Well, the answer is – yes.

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Big Data has already established itself as a serious challenge, and businesses all over the world are looking for the best ways to manage and analyze it.

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What is Search Analytics? Wikipedia defines Search Analytics as "Search analytics is the use of search data to investigate particular interactions among Web searchers, the search engine, or the content during searching episodes."

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Enterprise data is unstructured and complex. Because this data cannot be evaluated using typical keyword-based methods, it must be analysed using the most powerful data mining and text analysis technology available.

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The computation of similarity between phrases, sentences, or texts that have the same meaning but are not lexicographically comparable is known as textual semantic similarity measurement.

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There are many aspects to Big Data that make it so much more significant to the enterprises of today. Analysis of Big Data empowers organizations today to make informed business decisions based on the insights derived from Big Data.

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We all have heard the term 'Big Data' and Big Data is one of the most significant terms in the current times. Data experts say that enterprises today need to leverage the power of Big Data in order to gain the competitive edge and make informed decisions.

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With the implementation of many new technologies in the last decade, enterprise search has come a long way. Today's enterprise search tools are capable of analyzing even the most complicated organizational results.

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In an era when enterprises are struggling with managing the large volumes of enterprise data, text mining is something that every enterprise needs today. Text mining platforms can help enterprises streamline data analysis and management and also, make the best use of data by leveraging its potential for business growth.

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This is the era of Big Data. Every enterprise today is dealing with large volumes of data gathered from various sources. One of the biggest challenges faced by the enterprises today is, in fact, the analysis of this enormous volume of data.

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Most of us who are enthusiastic about enterprise search and big data are aware of the concept of natural language processing.

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Imagine being in a situation where you are standing in the middle of a room, with tall cabinets in every corner of the room filled with enormous volumes of documents in an ancient script that you have no clue of.

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What is artificial intelligence? Wikipedia defines it as "Artificial intelligence (AI), is intelligence demonstrated by machines, unlike the natural intelligence displayed by humans and animals, which involves consciousness and emotionality."

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Data is undoubtedly one of the most significant factors affecting the world today. Individuals as well as enterprises today are affected by data. Digitalization of everything around us has only added to the gigantic volumes of data that is being generated every minute.

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Semantic search is an entirely different approach when it compared to the more common keyword-based search approach that relies on matching keywords in the user’s query to the search results to find relevant results.

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Did you know that Big Data is all set to grow into a 103 billion dollar industry by 2023? Well, this makes Big Data one of the key domains to watch out for in the upcoming years. Today, enterprises are struggling to make use of Big Data to their advantage.

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We all have experienced how a short summary can make it easy for us to understand what to expect in a book or a comprehensive write-up. After all, it saves the reader's time and he or she can quickly understand what a large volume of content is all about.

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What is content classification? Content classification is defined as the process of classifying the content on the basis of predefined categories. It can be done manually or with the help of algorithms.

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Sentiment analysis is the technology that determines the exact sentiment behind a text or content. Also referred to as opinion mining, sentiment analysis is based on artificial intelligence (AI) and makes use of NLP, linguistics, and advanced text analysis to come to the right conclusion about the sentiment behind the said piece of content.

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What is data discovery? Well, data discovery is a process which is all about processing and analyzing the large variety of enterprise data for key insights.

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Enterprise search engines are key to the success of enterprises today because they help enterprises analyze and make the best use of the large volumes of data.

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What is enterprise data and how is it different from the data that we see and come across on the World Wide Web? Well, the differences are quite a handful, but one of the key differences is that while the data on the internet is optimised for the search engines, enterprise data is unstructured and non-optimised.

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Analyzing enterprise data is one of the biggest challenges faced by enterprises today, and it is because the enterprise data is complex and unstructured.

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Data is becoming an all-encompassing challenge today for every enterprise across different domains. As data continues to grow, it gets more and more challenging for enterprises to be able to analyze this data in a streamlined manner so as to be able to extract the information hidden within this data.

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Wikipedia defines Semantic search as "Semantic search seeks to improve search accuracy by understanding the searcher's intent and the contextual meaning of terms as they appear in the searchable dataspace, whether on the Web or within a closed system, to generate more relevant results."

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As the world moves towards digitalization, data is emerging as the most significant asset for enterprises across the globe. This has made enterprises worldwide seriously consider investing in technology that empowers them to make the best use of the large volumes of enterprise data.

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Given the exponential growth in medical literature, finding relevant information sooner is critical. Researchers, with more content and less time to analyse, 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.

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Big Data is a term that was coined fairly recently (in the 1990s). It refers to data sets that are significantly larger and more complex, and hence beyond the scope of conventional search and Data Analytics tools and software.

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Today enterprises across the globe are banking on the large volumes of enterprise data in order to draw insights for informed decision making.

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This is the age of Big Data and many data-centric organizations spend significant amounts of time and resources to acquire, aggregate, clean, enrich, standardize and manage data.

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This is the age of Big Data and many data-centric organizations spend significant amounts of time and resources to acquire, aggregate, clean, enrich, standardize and manage data.

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Organizations face the challenge of making the best possible use of their data while dealing with dynamic and changing data and also responding to business demand for faster time to market.

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Artificial intelligence (AI) is the technology that makes machines capable of simulating the cognitive abilities of the human mind, making them exhibit problem-solving, learning and basic decision-making capabilities – something that is not usually expected from a machine.

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One of the biggest challenges faced by enterprises today is the explosive growth of data. We live in the age of information and data has emerged as the biggest driving force for enterprises today.

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Artificial intelligence (AI) is the technology that makes machines capable of simulating the cognitive abilities of the human mind, making them exhibit problem-solving, learning and basic decision-making capabilities – something that is not usually expected from a machine.

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Imagine a situation where you have to interact with an acquaintance or a business associate from another country. What do you think can be the biggest challenge in communication? Well, we know it's the language.

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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.

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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.

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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.

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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.

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When it comes to reaching out to people and meeting the user's needs and expectations at the same time, we all agree that Mobile is the best way and there's no doubt about it.

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Data and analytics is all set to become more objective oriented, and this will hold true for industries across domains.

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Unstructured data is fundamentally different from structured data, because while structured data is generated by computers, unstructured data is generated by humans..

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When we look at the title, the first thing that comes to our mind is 'What are the 4 Cs? Well, the 4 Cs we're talking about here are Connection, Collaboration, Communication, and Customers.

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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.

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This statistic by Forrester research indicates that an intelligent search tool is the need of the hour, if organizations want to improve the efficiency and productivity of employees.

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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.

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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.

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We live in the times of Big Data and every enterprise in every corner of the globe today is facing the challenge of the unsurmountable volumes of data that keeps on increasing with every passing day.

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Natural language processing is all about making computers understand queries in the human language - the language you and me speak - and also respond in our language.

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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.

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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.

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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.

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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!!

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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.

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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.

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If you have multiple clients updating documents, it's really critical to ensure that a 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 updating of documents.

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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:

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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.

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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. To try this out in Solr 5.2, the machine was configured accordingly…..

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According to the official Docker website, "Docker is an open platform for developers and system admins 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".

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There are two ways to build docker image:

1. Running an image, modifying and committing it. This requires to access a live container.
2. Using a Docker file and building it

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