Infosys announced the launch of Infosys Mana, a platform that brings machine learning together with the deep knowledge of an organization, to drive automation and innovation – enabling businesses to continuously reinvent their system landscapes. According to the press release, Mana, with the Infosys Aikido service offerings, dramatically lowers the cost of maintenance for both physical and digital assets; captures the knowledge and know-how of people, and fragmented and complex systems; simplifies the continuous renovation of core business processes; and enables businesses to bring new and delightful user experiences leveraging state of the art technology. Over the last 35 years, Infosys has maintained, operated and managed systems with global clients across every industry. Building on this deep experience, Infosys has recognized the need to bring artificial intelligence to the enterprise in a meaningful and purposeful way; in a way that leverages the power of automation for repetitive tasks and frees people to focus on the higher value work, and on breakthrough innovation. Today's AI technologies address part of this with learning and information.

In today's post, we dive into understanding Association Rules for Market Basket Analysis and discuss three numeric measures that should be considered before deciding to act on / make a business decision based on associations that have been observed in the data: (1) Support (2) Confidence and (3) Lift. Association rules are typically written in the format: Left hand side Implies Right hand side The left hand side is referred to as the Antecedent and the right hand side is the Consequent. The Antecedent means a thing that logically precedes another while a Consequent means a thing that follows as a result. For example, in the association rule: {Butter, Eggs} Implies {Bread} Butter and eggs are the Antecedent while Bread is the Consequent. What this rule means that if you were to pick a shopping cart at random and find butter and eggs in there, there is a chance that you are also likely to find bread.

San Francisco – April 28, 2016: Infosys (NYSE: INFY), a global leader in consulting, technology, and next-generation services, today announced the launch of Infosys Mana, a platform that brings machine learning together with the deep knowledge of an organization, to drive automation and innovation – enabling businesses to continuously reinvent their system landscapes. Mana, with the Infosys Aikido service offerings, dramatically lowers the cost of maintenance for both physical and digital assets; captures the knowledge and know-how of people, and fragmented and complex systems; simplifies the continuous renovation of core business processes; and enables businesses to bring new and delightful user experiences leveraging state of the art technology. Over the last 35 years, Infosys has maintained, operated and managed systems with global clients across every industry. Building on this deep experience, Infosys has recognized the need to bring artificial intelligence to the enterprise in a meaningful and purposeful way; in a way that leverages the power of automation for repetitive tasks and frees people to focus on the higher value work, and on breakthrough innovation. Today's AI technologies address part of this with learning and information; Infosys is now bringing this together in a fundamental way with knowledge and understanding of the business and the IT landscape – critical knowledge that is locked inside source code, application silos, maintenance logs, exception tickets and individual employees.

Infosys (NYSE: INFY), a global leader in consulting, technology, and next-generation services, announced the launch of Infosys Mana, a platform that brings machine learning together with the deep knowledge of an organization, to drive automation and innovation – enabling businesses to continuously reinvent their system landscapes. Mana, with the Infosys Aikido service offerings, dramatically lowers the cost of maintenance for both physical and digital assets; captures the knowledge and know-how of people and fragmented and complex systems; simplifies the continuous renovation of core business processes; and enables businesses to bring new and delightful user experiences leveraging state-of-the-art technology. Over the last 35 years, Infosys has maintained, operated and managed systems with global clients across every industry. Building on this deep experience, Infosys has recognized the need to bring artificial intelligence to the enterprise in a meaningful and purposeful way; in a way that leverages the power of automation for repetitive tasks and frees people to focus on the higher value work, and on breakthrough innovation. Today's AI technologies address part of this with learning and information; Infosys is now bringing this together in a fundamental way with knowledge and understanding of the business and the IT landscape – critical knowledge that is locked inside source code, application silos, maintenance logs, exception tickets and individual employees.

Consulting, technology, and next-generation services company, Infosys, has launched its knowledge-based artificial intelligence platform. Named Infosys Mana, it is a platform that brings machine learning together with the deep knowledge of an organisation, to drive automation and innovation, enabling businesses to continuously reinvent their system landscapes. Mana, with the Infosys Aikido service offerings, aims to lower the cost of maintenance for both physical and digital assets; captures the knowledge and know-how of people, and fragmented and complex systems; simplifies the continuous renovation of core business processes; and enables businesses to bring new user experiences by leveraging technology. Infosys Mana is comprised of three integrated components all of which are based on open source technology – the Infosys Information Platform; Infosys Automation Platform; and Infosys Knowledge Platform. Infosys managing director and CEO, Dr. Vishal Sikka, said Infosys has recognised the need to bring artificial intelligence to the enterprise in a meaningful and purposeful way; in a way that leverages the power of automation for repetitive tasks and frees people to focus on the higher value work, and on innovation.

A referring expression in linguistics is any noun phrase identifying an object in a way that will be useful to interlocutors. In the context of a query over a first order knowledge base K, constant symbols occurring in K are the artifacts usually used as referring expressions in certain answers to the query. In this paper, we begin to explore how this can be usefully extended by allowing a class of more general formulas, called Singular Referring Expressions, to replace constants in this role. In particular, we lay a foundation for admitting Singular Referring Expressions in certain answer computation for queries over K. An integral part of this foundation are characterization theorems for identification properties of Singular Referring Expressions for queries annotated with a domain specific language for referring concept types. Finally, we apply this framework in the context of tractable description logic dialects, showing how identification properties can be determined at compile-time for conjunctive queries, and how off-the-shelf conjunctive query evaluation for these dialects can be used in query evaluations, preserving, in all cases, underlying tractability.

In this paper, we propose a general framework, both parameterized and parameter-free, for defining a family of fine-grained inconsistency measures for propositional knowledge bases. The parameterized approach allows to encompass several existing inconsistency mea- sures as specific cases, by properly setting its parameter. And the parameter-free approach is defined to avoid the difficulty in choosing a suitable parameter in practice but still keeps a desired ranking for knowledge bases by their inconsistency degrees. The fine granularity of our framework is based on the notion of MIS partition that considers the inner structure of all the minimal inconsistent subsets of a knowledge base. Moreover, MinCostSAT-based encodings are provided, which enable the use of efficient SAT solvers for the computation of the proposed measures. We implement these algo- rithms and test them on some real-world datasets. The preliminary experimental results for a variety of inputs show that the proposed framework gives a wide range of possibilities for evaluating large knowledge bases.

The evaluation of Datalog rules over large Knowledge Graphs (KGs) is essential for many applications. In this paper, we present a new method of materializing Datalog inferences, which combines a column-based memory layout with novel optimization methods that avoid redundant inferences at runtime. The pro-active caching of certain subqueries further increases efficiency. Our empirical evaluation shows that this approach can often match or even surpass the performance of state-of-the-art systems, especially under restricted resources.

In this demo we present a semantic framework for innovation and patent analytics powered by Mined Semantic Analysis (MSA). Our framework provides cognitive assistance to its users through a Web-based visual and interactive interface. First, we describe building a conceptual knowledge graph by mining user-generated encyclopedic textual corpus for semantic associations. Then, we demonstrate applying the acquired knowledge to support many cognition and knowledge based use cases for innovation analysis including technology exploration and landscaping, competitive analysis, literature and prior art search and others.

Privacy preserving in association mining is an important research topic in the database security field. This paper has proposed a blocking-based method to solve the association rule hiding problem for data sharing. It aims at reducing undesirable side effects and increasing desirable side effects, while ensuring to conceal all sensitive rules. The candidate transactions are selected for sanitization based on their relations with border rules. Comparative experiments on real datasets demonstrate that the proposed method can achieve its goals.