Semantic search functionality (Cognitive computing) and social media data UIMA, NLP, Elasticsearch/Solr	 How to teach computers to observe, evaluate and decide.

Semantic search functionality (Cognitive computing) and social media data UIMA, NLP, Elasticsearch/Solr How to teach computers to observe, evaluate and decide.

Consider an example from our daily life. When we enter into an unfamiliar dark room, and try to turn on the light, our memory informs us the most likely place in a room a light switch can be at. This memory is created from the reminder of several repeated past incidents when we had switched on lights in darkened rooms. We reach at the place in the dark room where the switch is to be found possibly, and we almost certainly find it. Human brain is an assemblage of associational activities. The brain gathers associational attributes that connect various data entities, and then collect them into common frameworks where these associations appear.
Cognitive computing platform is built in much of the same manner. “The technology actively learns ‘patterns’ of association and then reasons based upon what it has learned from these associational patterns.”
Cognitive computing platform implements machine learning algorithms and repeatedly acquire memory from the data provided to them by mining data for information. The systems perfect the approach they try to find patterns along with the approach they process data so that they are adept in anticipating new problems and forming likely solutions.
UMAI (Unstructured Information Management Applications) is an open source framework that studies huge volume of unstructured information (e.g., text, speech) to determine knowledge that is relevant to an end user. UIMA furthermore is responsible for competencies to wrap components as network services, and can scale to very large volumes by replicating processing pipelines over a cluster of networked nodes.
Solr is very consistent, accessible and error tolerant, that provides disseminated indexing, replication and load-balanced querying, instinctive failover and recovery, central formation among others. Solr impacts the pursuit and navigation features of various major internet sites of the world. Solr is rightly open-source–community over code. Everyone can contribute to Solr. A novice Solr developer (aka committers) is chosen based on excellence. Elasticsearch is only theoretically open-source. Everybody can view the source, every may contribute to its changes, but only employees of Elasticsearch can in reality make variations to Elasticsearch.
A challenge before the approach is, human can easily comprehend fellow human’s emotion, but can a machine be trained to do so? To detect the emotion of the persons involved in the conversation, the transcript of such conversation needs to be made. Then the emotions involved should be classified under “normal or neutral” and “agitated or excited”. Then, the probability of whether the conversation as successful or not is computed.
The aim of the Natural Language Processing (NLP) algorithm is to design and build software that can be able to analyse, comprehend, and create languages that can be used by the humans naturally, in such a manner that humans will be able to communicate with the computers as though they were communicating to other humans and vice versa.

Big data Analytics using Natural Language Processing algorithms to process Social media data and present it with a simple visualization platform to the customers.

Big data Analytics using Natural Language Processing algorithms to process Social media data and present it with a simple visualization platform to the customers.

It’s said, “Simple algorithm with big data is better than complex algorithm with small data”. Computers understand structures languages known as coding. It’s ironic that natural language which is mostly unstructured is easiest for humans to learn and use, is toughest for a computer to master it.
In case of language used in social media, the data used is not only huge, but lots of abbreviations are also used such as lol, ASAP, etc. Apart from it, in language used here, is human language; it contains different words to be interpreted by the computer, along with lots of emotions, sentiments, as well as altered meaning. Further, the biggest challenge in such conversation is that, “often a lot more is told by what is not said in a sentence or conversation than what is said.”
Such case poses another major challenge such as; the narrator/communicator could deliberately skew the outcome. “For example, one may pretend to be angry, or one may also lie. Thus, there can be a many false positives involved. The primary task will be to categorize or estimate the emotions of the communicators and then compute the probability of whether the conversation as successful or not.
Thus, in these cases, the measure of quantity of data is important, but the quality of data is much more vital. The first step will be to make sense of what is communicated. To do this, a very clever algorithm will be required. The algorithm should be modern algorithm, so that, it can interpret the modern form of communicative languages used in social media. Next the task will be to detect the emotion of the persons involved in the conversation. If the form of communication in the social media is in audio format, transcript of such conversation needs to be made. Then the emotions involved should be classified under “normal or neutral” and “agitated or excited”. Then, the probability of whether the conversation as successful or not is computed.
The aim of the Natural Language Processing (NLP) algorithm is to design and build software that can be able to analyse, comprehend, and create languages that can be used by the humans naturally, in such a manner that humans will be able to communicate with the computers as though they were communicating to other humans and vice versa.
Using Natural Language Processing algorithms to process Social media data and present it with a simple visualization platform involves use of a combination of knowledge-engineered and statistical/machine-learning techniques to disambiguate and return to natural human language input. It will involve implications for applications like text critiquing, data recovery, query replying, summarization, and translation. Further, the interpreted data has to be represented in visualization platform for the users

How IBM Watson is going to change the lives of billions in healthcare and retail?

IBM Watson has truly proved that change is the law of nature. And it has changed the whole scenario of older applications and devices. Thanks to IBM for such a wonderful, unique and awesome cognitive technology!
With the help of Watson, enterprises from healthcare to retail can easily evolve their present processes and can gain advantages above and beyond their expectations. Yes, by utilizing Watson in healthcare industry, doctors or medical professionals can fetch out medical information quickly and they can diagnose best medical treatments through medical studies or clinical references. It means Watson simplifies processes between humans and computers in easy and natural manner. The reason is it is able to read and understand natural language from unstructured data that is crucial and it contains up to 80 per cent data.
So, whether you are related to healthcare or retail sectors, this ecosystem is a best solution in order to provide your customers & users with creativity with a difference. In the humdrum of hectic schedules, customers, clients or users want a safe and secure ways that is trustworthy and accurate. That is what Watson does. With its repeated usage, it becomes smarter as it tracts feedbacks & responses from its users and thus provides more accurate & precise information that is relevant and useful.
It won’t be exaggeration if entrepreneurs or enterprises say that with the advent of Watson, new era of cognitive apps has begun. There are absolute reasons for it. Watson uses programmatic computing along with three most important capabilities such as natural language processing, hypothesis generation & evaluation and a dynamic learning.
Really, Watson is a magic-wand for enterprises. If any enterprise wants a right solution for itself then there isn’t a better option than Watson. Depending upon the requirements of your organizations, you can use Watson Explorer, Watson Discovery Advisor or Watson Engagement Advisor in order to increase customer loyalty. Whether you wish unique insights, quick information for research or want to bridge the gap between you and your customers, Watson serves all purposes without letting a single stone unturned.
Moreover, through its valuable partnership, developers can build with Watson new generation apps and accelerate innovation. Watson has simply smoothened the content curation. It helps retail or healthcare enterprises go deeper into subject in order to get more relevant data that is never thought before.
Above all, Watson is a mentor for retailers, planners, doctors and government. For example, using Watson, a retailer can take proper decision of pricing and purchasing avoiding unnecessary data. Also, an oncologist can easily identify treatment options that are evidence-based like published research, cancer case history etc. In this way, doctors can personalize patients’ care while using Watson.
Thus, Watson undoubtedly excels the lives of billions in healthcare and retail industries.

Enterprise Mobile Application Development

Enterprise Mobile Application Development

In the Informational Technology age, innovators are trying their best in smoothening business processes by developing new applications. Enterprise mobile application development is one of the best examples of such innovations.
Whether you belong to a small scale business or a big enterprise, enterprise mobile application development can be an integral part of your esteemed organizations. Yes, there isn’t any doubt about it. It is a tool which simplifies business processes and simultaneously it helps increase productivity in abundance.
Enterprise mobile app development has accelerated its speed since the inception of Smartphones and tablets. And seeing a better future using these modern gadgets, enterprises have started its usage in a wide scale. They are now able to fulfill the objective of delivering task focused applications for employees in order to ease their jobs or tasks.
Thus, IT companies are getting enough benefits from enterprise mobile application development. With the help of mobile app development, businesses can reach to thousands of potential customers. The reason is there are millions of users who use Smartphones and tablets in order to purchase products, avail services or just to connect with their friends and colleagues.
By creating customized applications, companies can engage their employees as well as customers. Personalized mobile applications are today’s utmost requirement. That is why enterprise mobile application development has geared up the market with its fascinating usage and results.
Whether a user is on the way, on a beach or in the office, he/she can pay the bills, book the tickets and do much more using user-friendly mobile apps development. So, it is essential to develop such applications with clear insight and targeting the specific audiences. It should be easy to use and user-friendly. Above all, it should be compatible to the modern mobile platforms such as Android, Windows Mobile, Symbian, BREW, Web OS, Mobile Web, J2ME, and BlackBerry.
Companies and organizations which are using enterprise mobile apps development have witnessed incredible customer hype along with good revenue growth. So, this is one of the best and easiest ways to reach to the potential customers.
Because of its high demand in the market, IT companies have started building customized enterprise mobile application development for their customers and clients. Though it is expensive, developing businesses are investing in mobile app development because it provides unexpected results in terms of user engagement and profit.
But before you use any of the enterprise mobile application development, be sure that it is not flashy and complicated. Otherwise, you may suffer a heavy loss. Yes, you can lose your customers if it is not properly coded and doesn’t serve the purposes. It means it should provide the desired results or functions in the simplest and easiest ways with easy-to-navigate interface.
Above all, to get maximum benefits out of enterprise mobile application development, it should be used at a proper time and in a very secure fashion!

Intellectyx sponsors Elastic{ON}15 – conference dedicated towards ElasticSearch, Logstash, Kibana stack

Intellectyx sponsors Elastic{ON}15 – conference dedicated towards ElasticSearch, Logstash, Kibana stack

We are thrilled to announce Intellectyx as one of the golden sponsor of Elastic{ON}15, the first of its kind conference entirely dedicated to ELK (ElasticSearch, Logstash, Kibana stack). It takes place in San Francisco, CA, from March March 9th to 11th. With extensive elasticseach development experience on our belt, we are looking forward to share our experience and connect with other industry players to hear their stories of using Elastic search, Logstash, and Kibana to make sense of data.

Also we will be doing live demos on some of the ELK stack applications we have built with various clients and showcase the drastic higher performance difference of ELK stack vs. traditional application stack environments. If you are interested in learning more about our work in ELK stack and how we can help you, please contact us

Growing Business Changes and IT Challenges

Growing Business Changes and IT Challenges

The management of business and operations in organizations is becoming more challenging and complex. Effectively managing and working with this complexity means IT organizations are called on to provide business-intelligence-related capabilities for understanding where and how value is created in the business, and responding more quickly to market changes and opportunities. These macro business changes are altering the way organizations view Information Technology including business intelligence, which will affect how they view and support an information management infrastructure. These business changes include:

  • Multiple and interlinked strategies and metrics: Organizations are striving to link multiple strategies and metrics (innovation, customer creation, management, operational efficiency and effectiveness, and financial performance) to drive transformation.
  • Ever-increasing rates of change: The speed of change and the occurrence of multiple business cycles, product development, delivery models and markets have increased the rate of evolution. With new partners, delivery models and markets (geographic and industry), businesses are constantly changing (sense and respond).
  • Fragmented planning systems: The sophistication of planning systems has increased such that a myriad of solutions exists for all aspects of planning, from demand and supply chain planning to marketing planning and financial budgeting. These systems are being deployed tactically, which makes it difficult to understand the impact of different aspects of corporate activities on overall performance.
  • Increased scrutiny: The compliance demands resulting from various regulations and other governance regulations mean that information and decision-making processes are subject to the same degree of scrutiny as transaction processing. Organizations are striving for greater transparency and balancing this against privacy and integrity demands.
  • Consumerization of IT: Increasingly, users at all levels expect to have access to business, entertainment, government and personal information, similar to how they use tools such as Google to search the Web. This places increasing pressure on IT to respond more quickly to a growing demand from users for web-based solutions that are intuitive and easy to use.. IT organizations need to incorporate these user requirements and behaviors into their business intelligence strategies and governance policies concerning the use and access to information.