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Swarm intelligence (SI) is the collective behavior of decentralized, self-organized systems, natural or artificial.
The agents follow very simple rules, and although there is no centralized control structure dictating how individual agents should behave, local, and to a certain degree random, interactions between such agents lead to the emergence of 'intelligent' global behavior, unknown to the individual agents.
Examples of swarm intelligence in natural systems include ant colonies, bird flocking, animal herding, bacterial growth, fish schooling and microbial intelligence.
A swarm is modelled in SPP by a collection of particles that move with a constant speed but respond to a random perturbation by adopting at each time increment the average direction of motion of the other particles in their local neighbourhood.
A large number of more recent metaphor-inspired metaheuristics have started to attract criticism in the research community for hiding their lack of novelty behind an elaborate metaphor.
SDS is an agent-based probabilistic global search and optimization technique best suited to problems where the objective function can be decomposed into multiple independent partial-functions.
Each agent maintains a hypothesis which is iteratively tested by evaluating a randomly selected partial objective function parameterised by the agent's current hypothesis.
Unlike the stigmergic communication used in ACO, in SDS agents communicate hypotheses via a one-to-one communication strategy analogous to the tandem running procedure observed in Leptothorax acervorum.
The simulated 'ants' similarly record their positions and the quality of their solutions, so that in later simulation iterations more ants locate for better solutions.
Particle swarm optimization (PSO) is a global optimization algorithm for dealing with problems in which a best solution can be represented as a point or surface in an n-dimensional space.
The main advantage of such an approach over other global minimization strategies such as simulated annealing is that the large number of members that make up the particle swarm make the technique impressively resilient to the problem of local minima.
Basically, this uses a probabilistic routing table rewarding/reinforcing the route successfully traversed by each 'ant' (a small control packet) which flood the network.
A very different-ant inspired swarm intelligence algorithm, stochastic diffusion search (SDS), has been successfully used to provide a general model for this problem, related to circle packing and set covering.
Lawson used an ant-based computer simulation employing only six interaction rules to evaluate boarding times using various boarding methods.(Miller, 2010, xii-xviii).
Enabled by mediating software such as the SWARM platform (formally unu) from Unanimous A.I., networks of distributed users can be organized into 'human swarms' through the implementation of real-time closed-loop control systems.
As published by Rosenberg (2015), such real-time systems enable groups of human participants to behave as a unified collective intelligence that works as a single entity to make predictions, answer questions, and evoke opinions.
Such systems, also referred to as 'Artificial Swarm Intelligence' (or the brand name Swarm AI) have been shown to significantly amplify human intelligence, resulting in a string of high-profile predictions of extreme accuracy.
have successfully used two swarm intelligence algorithms—one mimicking the behaviour of one species of ants (Leptothorax acervorum) foraging (stochastic diffusion search, SDS) and the other algorithm mimicking the behaviour of birds flocking (particle swarm optimization, PSO)—to describe a novel integration strategy exploiting the local search properties of the PSO with global SDS behaviour.
The resulting hybrid algorithm is used to sketch novel drawings of an input image, exploiting an artistic tension between the local behaviour of the 'birds flocking'—as they seek to follow the input sketch—and the global behaviour of the 'ants foraging'—as they seek to encourage the flock to explore novel regions of the canvas.
Having associated the rendering process with the concepts of attention, the performance of the participating swarms creates a unique, non-identical sketch each time the 'artist' swarms embark on interpreting the input line drawings.
Common functions of business intelligence technologies include reporting, online analytical processing, analytics, data mining, process mining, complex event processing, business performance management, benchmarking, text mining, predictive analytics and prescriptive analytics.
BI technologies can handle large amounts of structured and sometimes unstructured data to help identify, develop and otherwise create new strategic business opportunities.
In all cases, BI is most effective when it combines data derived from the market in which a company operates (external data) with data from company sources internal to the business such as financial and operations data (internal data).
Amongst myriad uses, business intelligence tools empower organizations to gain insight into new markets, to assess demand and suitability of products and services for different market segments and to gauge the impact of marketing efforts.
When Hans Peter Luhn, a researcher at IBM, used the term business intelligence in an article published in 1958, he employed the Webster's Dictionary definition of intelligence: 'the ability to apprehend the interrelationships of presented facts in such a way as to guide action towards a desired goal.'
In 1989, Howard Dresner (later a Gartner analyst) proposed business intelligence as an umbrella term to describe 'concepts and methods to improve business decision making by using fact-based support systems.'
According to Forrester Research, business intelligence is 'a set of methodologies, processes, architectures, and technologies that transform raw data into meaningful and useful information used to enable more effective strategic, tactical, and operational insights and decision-making.'
Under this definition, business intelligence encompasses information management (data integration, data quality, data warehousing, master-data management, text- and content-analytics, et al.).
Though the term business intelligence is sometimes a synonym for competitive intelligence (because they both support decision making), BI uses technologies, processes, and applications to analyze mostly internal, structured data and business processes while competitive intelligence gathers, analyzes and disseminates information with a topical focus on company competitors.
Thomas Davenport, professor of information technology and management at Babson College argues that business intelligence should be divided into querying, reporting, Online analytical processing (OLAP), an 'alerts' tool, and business analytics.
Business operations can generate a very large amount of information in the form of e-mails, memos, notes from call-centers, news, user groups, chats, reports, web-pages, presentations, image-files, video-files, and marketing material.
Because of the difficulty of properly searching, finding and assessing unstructured or semi-structured data, organizations may not draw upon these vast reservoirs of information, which could influence a particular decision, task or project.
2009 Information Management special report predicted the top BI trends: 'green computing, social networking services, data visualization, mobile BI, predictive analytics, composite applications, cloud computing and multitouch'.
According to a study by the Aberdeen Group, there has been increasing interest in Software-as-a-Service (SaaS) business intelligence over the past years, with twice as many organizations using this deployment approach as one year ago – 15% in 2009 compared to 7% in 2008.
An article by InfoWorld's Chris Kanaracus points out similar growth data from research firm IDC, which predicts the SaaS BI market will grow 22 percent each year through 2013 thanks to increased product sophistication, strained IT budgets, and other factors.
- On Tuesday, February 18, 2020
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