Weak signal identification with semantic web mining

We investigate an automated identification of weak signals according to Ansoff to improve strategic planning and technological forecasting. Literature shows that weak signals can be found in the organization's environment and that they appear in different contexts. We use internet information to represent organization's environment and we select these websites that are related to a given hypothesis. In contrast to related research, a methodology is provided that uses latent semantic indexing (LSI) for the identification of weak signals. This improves existing knowledge based approaches because LSI considers the aspects of meaning and thus, it is able to identify similar textual patterns in different contexts. A new weak signal maximization approach is introduced that replaces the commonly used prediction modeling approach in LSI. It enables to calculate the largest number of relevant weak signals represented by singular value decomposition (SVD) dimensions. A case study identifies and analyses weak signals to predict trends in the field of on-site medical oxygen production. This supports the planning of research and development (R&D) for a medical oxygen supplier. As a result, it is shown that the proposed methodology enables organizations to identify weak signals from the internet for a given hypothesis. This helps strategic planners to react ahead of time

Mining the Change of Events in Environmental Scanning for Decision Support

An organization’s environment is increasingly complex. Business demand on environmental scanning has significantly increased in recent years due to an attempt to assist management in planning an organization’s strategies and responses. The conventional technique for environmental scanning is event detection from text documents such as news stories. Event detection methods recognize events while they neglect to discover the changes of events. This work develops an event change detection (ECD) approach that combines association rule mining and change mining techniques. Detecting changes of events aids managers in making fast responses to the change of external environments. Association rule mining is employed to discover the subject behaviors of events from news stories. Changes of events are identified by comparing the subject behaviors of events from different time periods. The discovered event changes can provide effective decision support for decision makers to capture environmental information in a timely manner and make adequate decisions

Results of Environmental Scanning Applied to the Design of a Deer Management Decision Support System (DSS) For The United States and California

Using freely available internet search tools for environmental scanning, information related to deer management was collected, categorized, and evaluated with the goal of providing public decision support. Key issues raised in the public debate discovered by the search are addressed with relevant information formatted as output for a decision support system – dashboard elements. A graph addresses contradictory reports about the current direction of the deer population; the trend since 2006 appears to be down. Another graph illustrates the approximate longterm population trend; the current U.S. white-tailed deer population is about the same as in 1500. A table summarizes profiles of state deer issues and strategies. Only eleven states are trying to reduce their deer population. A graph illustrates the rise and fall of the California population, the most dramatic population decline in the U.S. over the past 100 years. Hunting pressure and herd demographic management are found to be related to the decline, making these candidate variables for attention in the decision support system. This case application is designed to illustrate methods the author has learned in creating a variety of decision support applications for technology companies

Towards 2000: A Tougher Future for Australian Business?

The paper reports on the future business environment expected by top level Australian executives. It forecasts environmental changes to the year 2000 and updates projections reported in an earlier study. Specifically, top managers from 171 of Australia\u27s largest 500 corporations provide their views concerning world ecology, the economy, technology and political-social developments. Their perceptions are then linked to specific competitive strategies that are evoked by the long-range forecast which they expect

A comparison of theory and practice in market intelligence gathering for Australian micro-businesses and SMEs

Recent government sponsored research has demonstrated that there is a gap between the theory and practice of market intelligence gathering within the Australian micro, small and medium businesses (SMEs). Typically, there is a significant amount of information in literature about 'what needs to be done', however, there is little insight in terms of how market intelligence gathering should occur. This paper provides a novel insight and a comparison between the theory and practices of market intelligence gathering of micro-business and SMEs in Australia and demonstrates an anomoly in so far as typically the literature does not match what actually occurs in practice. A model for market intelligence gathering for micro-businesses and SMEs is also discussed

A comparison of theory and practice in market intelligence gathering for Australian micro-businesses and SMEs

Recent government sponsored research has demonstrated that there is a gap between the theory and practice of market intelligence gathering within the Australian micro, small and medium businesses (SMEs). Typically, there is a significant amount of information in literature about 'what needs to be done', however, there is little insight in terms of how market intelligence gathering should occur. This paper provides a novel insight and a comparison between the theory and practices of market intelligence gathering of micro-business and SMEs in Australia and demonstrates an anomoly in so far as typically the literature does not match what actually occurs in practice. A model for market intelligence gathering for micro-businesses and SMEs is also discussed

A rule dynamics approach to event detection in Twitter with its application to sports and politics

The increasing popularity of Twitter as social network tool for opinion expression as well as informa- tion retrieval has resulted in the need to derive computational means to detect and track relevant top- ics/events in the network. The application of topic detection and tracking methods to tweets enable users to extract newsworthy content from the vast and somehow chaotic Twitter stream. In this paper, we ap- ply our technique named Transaction-based Rule Change Mining to extract newsworthy hashtag keywords present in tweets from two different domains namely; sports (The English FA Cup 2012) and politics (US Presidential Elections 2012 and Super Tuesday 2012). Noting the peculiar nature of event dynamics in these two domains, we apply different time-windows and update rates to each of the datasets in order to study their impact on performance. The performance effectiveness results reveal that our approach is able to accurately detect and track newsworthy content. In addition, the results show that the adaptation of the time-window exhibits better performance especially on the sports dataset, which can be attributed to the usually shorter duration of football events

Conservation science in NOAA’s National Marine Sanctuaries: description and recent accomplishments

This report describes cases relating to the management of national marine sanctuaries in which certain scientific information was required so managers could make decisions that effectively protected trust resources. The cases presented represent only a fraction of difficult issues that marine sanctuary managers deal with daily. They include, among others, problems related to wildlife disturbance, vessel routing, marine reserve placement, watershed management, oil spill response, and habitat restoration. Scientific approaches to address these problems vary significantly, and include literature surveys, data mining, field studies (monitoring, mapping, observations, and measurement), geospatial and biogeographic analysis, and modeling. In most cases there is also an element of expert consultation and collaboration among multiple partners, agencies with resource protection responsibilities, and other users and stakeholders. The resulting management responses may involve direct intervention (e.g., for spill response or habitat restoration issues), proposal of boundary alternatives for marine sanctuaries or reserves, changes in agency policy or regulations, making recommendations to other agencies with resource protection responsibilities, proposing changes to international or domestic shipping rules, or development of new education or outreach programs. (PDF contains 37 pages.

TRCM: a methodology for temporal analysis of evolving concepts in Twitter

The Twitter network has been labelled the most commonly used microblogging application around today. With about 500 million estimated registered users as of June, 2012, Twitter has become a credible medium of sentiment/opinion expression. It is also a notable medium for information dissemination; including breaking news on diverse issues since it was launched in 2007. Many organisations, individuals and even government bodies follow activities on the network in order to obtain knowledge on how their audience reacts to tweets that affect them. We can use postings on Twitter (known as tweets) to analyse patterns associated with events by detecting the dynamics of the tweets. A common way of labelling a tweet is by including a number of hashtags that describe its contents. Association Rule Mining can find the likelihood of co-occurrence of hashtags. In this paper, we propose the use of temporal Association Rule Mining to detect rule dynamics, and consequently dynamics of tweets. We coined our methodology Transaction-based Rule Change Mining (TRCM). A number of patterns are identifiable in these rule dynamics including, new rules, emerging rules, unexpected rules and ?dead' rules. Also the linkage between the different types of rule dynamics is investigated experimentally in this paper