15 Effective Tools for Visual Knowledge Management
Since I started my quest a few years ago searching for the ultimate knowledge management tool, I’ve discovered a number of interesting applications that help people efficiently organize information. There certainly is no shortage of solutions for this problem domain. Many tools exist that offer the ability to discover, save, organize, search, and retrieve information. However, I’ve noticed a trend in recent years, and some newer applications are focusing more on the visual representation and relationship of knowledge. Most traditional personal knowledge management (PKM) or personal information management (PIM) applications offer the same basic set of features: * Storage of notes and documents * Search functionality and keyword/tagging capability * Outline view in a traditional hierarchy, or user-defined views * Task management, calendar, and contact management (mainly PIM, not KM) These are essential features, however don’t offer too much to the more visually-inclined knowledge junkies.
Data Mining
Process of extracting and discovering patterns in large data sets Data mining is the process of extracting and discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems.[1] Data mining is an interdisciplinary subfield of computer science and statistics with an overall goal of extracting information (with intelligent methods) from a data set and transforming the information into a comprehensible structure for further use.[1][2][3][4] Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD.[5] Aside from the raw analysis step, it also involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating.[1] Etymology[edit] Background[edit] The manual extraction of patterns from data has occurred for centuries. Process[edit]
Knowledge retrieval
Knowledge Retrieval seeks to return information in a structured form, consistent with human cognitive processes as opposed to simple lists of data items. It draws on a range of fields including epistemology (theory of knowledge), cognitive psychology, cognitive neuroscience, logic and inference, machine learning and knowledge discovery, linguistics, and information technology. Overview[edit] In the field of retrieval systems, established approaches include: Data Retrieval Systems (DRS), such as database management systems, are well suitable for the storage and retrieval of structured data.Information Retrieval Systems (IRS), such as web search engines, are very effective in finding the relevant documents or web pages. Both approaches require a user to read and analyze often long lists of data sets or documents in order to extract meaning. The goal of knowledge retrieval systems is to reduce the burden of those processes by improved search and representation. References[edit]
IBM - Knowledge Discovery and Data Mining
Knowledge Discovery and Data Mining (KDD) is an interdisciplinary area focusing upon methodologies for extracting useful knowledge from data. The ongoing rapid growth of online data due to the Internet and the widespread use of databases have created an immense need for KDD methodologies. The challenge of extracting knowledge from data draws upon research in statistics, databases, pattern recognition, machine learning, data visualization, optimization, and high-performance computing, to deliver advanced business intelligence and web discovery solutions. IBM Research has been at the forefront of this exciting new area from the very beginning. With the explosive growth of online data and IBM’s expansion of offerings in services and consulting, data-based solutions are increasingly crucial.
Knowledge Management using Mind Maps
Click to download this Mind Map document. In this article, we’ll what a look at knowledge management. Actually more than just knowledge management – we’ll examine how knowledge is created, what it is, and how you can use it. In these days of information overload, the compact way of representing ideas that is embodied in Mind Mapping is essential. You can summarize a huge amount of information in a very compact space. From Data to Information We are fed with a huge amount of data all the time, and we are pretty good at sorting through the incoming data and applying our understanding of the relationships between the different pieces of data and its meaning to us, so we can turn the data into information. delete, distort, and filter the data to fit our understanding of the world. From Information to Knowledge This massively reduces the amount of material we need to deal with, and also helps us process new data as it comes along, but still we are overwhelmed with the amount of information.
Systems Engineering
Systems engineering techniques are used in complex projects: spacecraft design, computer chip design, robotics, software integration, and bridge building. Systems engineering uses a host of tools that include modeling and simulation, requirements analysis and scheduling to manage complexity. Systems engineering is an interdisciplinary field of engineering that focuses on how to design and manage complex engineering systems over their life cycles. The systems engineering process is a discovery process that is quite unlike a manufacturing process. History[edit] The term systems engineering can be traced back to Bell Telephone Laboratories in the 1940s.[1] The need to identify and manipulate the properties of a system as a whole, which in complex engineering projects may greatly differ from the sum of the parts' properties, motivated various industries to apply the discipline.[2] Concept[edit] Systems engineering signifies only an approach and, more recently, a discipline in engineering.
GURTEEN KNOWLEDGE
Inference Engine
An Inference Engine is a tool from Artificial Intelligence. The first inference engines were components of expert systems. The typical expert system consisted of a knowledge base and an inference engine. Architecture[edit] The logic that an inference engine uses is typically represented as IF-THEN rules. A simple example of Modus Ponens often used in introductory logic books is "If you are human then you are mortal". Rule1: Human(x) => Mortal(x) A trivial example of how this rule would be used in an inference engine is as follows. This innovation of integrating the inference engine with a user interface led to the second early advancement of expert systems: explanation capabilities. An inference engine cycles through three sequential steps: match rules, select rules, and execute rules. In the first step, match rules, the inference engine finds all of the rules that are triggered by the current contents of the knowledge base. Implementations[edit] See also[edit] References[edit]
