Road crash proneness prediction using data mining

Developing safe and sustainable road systems is a common goal in all countries. Applications to assist with road asset management and crash minimization are sought universally. This paper presents a data mining methodology using decision trees for modeling the crash proneness of road segments using available road and crash attributes. The models quantify the concept of crash proneness and demonstrate that road segments with only a few crashes have more in common with non-crash roads than roads with higher crash counts. This paper also examines ways of dealing with highly unbalanced data sets encountered in the study.

Text mining and probabilistic language modeling for online review spam detecting

In the era of Web 2.0, huge volumes of consumer reviews are posted to the Internet every day. Manual approaches to detecting and analyzing fake reviews (i.e., spam) are not practical due to the problem of information overload. However, the design and development of automated methods of detecting fake reviews is a challenging research problem. The main reason is that fake reviews are specifically composed to mislead readers, so they may appear the same as legitimate reviews (i.e., ham). As a result, discriminatory features that would enable individual reviews to be classified as spam or ham may not be available. Guided by the design science research methodology, the main contribution of this study is the design and instantiation of novel computational models for detecting fake reviews. In particular, a novel text mining model is developed and integrated into a semantic language model for the detection of untruthful reviews. The models are then evaluated based on a real-world dataset collected from amazon.com. The results of our experiments confirm that the proposed models outperform other well-known baseline models in detecting fake reviews. To the best of our knowledge, the work discussed in this article represents the first successful attempt to apply text mining methods and semantic language models to the detection of fake consumer reviews. A managerial implication of our research is that firms can apply our design artifacts to monitor online consumer reviews to develop effective marketing or product design strategies based on genuine consumer feedback posted to the Internet.

A data mining driven risk profiling method for road asset management

Road surface skid resistance has been shown to have a strong relationship to road crash risk, however, applying the current method of using investigatory levels to identify crash prone roads is problematic as they may fail in identifying risky roads outside of the norm. The proposed method analyses a complex and formerly impenetrable volume of data from roads and crashes using data mining. This method rapidly identifies roads with elevated crash-rate, potentially due to skid resistance deficit, for investigation. A hypothetical skid resistance/crash risk curve is developed for each road segment, driven by the model deployed in a novel regression tree extrapolation method. The method potentially solves the problem of missing skid resistance values which occurs during network-wide crash analysis, and allows risk assessment of the major proportion of roads without skid resistance values.

Gender, sexuality, and rurality in the mining industry

Sexuality is a subject that has been, at best, marginal in the significant body of literature that has examined gender and mining in contemporary Western nations. This is despite the fact that academics have circled, if not almost bumped into the topic in closely related discussions of hegemonic masculinity and mining work, and of patriarchal familial relations and mining communities. This scholarship has documented what has been and remains women’s primary relationship to mining—that is, as a “mining wife.” How patriarchal relations are manifest in and emerge from this state of affairs has been critiqued with research on the gendered implications of housing arrangements in mining towns, the division of household labor, changing shift-work mining rosters, and the gendered consequences of strikes and mine closures (Williams 1981; Gibson 1992; Gibson-Graham 1996; Rhodes 2005; McDonald, Mayes, and Pini 2012). Despite the centrality of the heterosexual relationship—and indeed heteronormativity—to these discussions, scholars of gender and mining have had little to say on the subject of sexuality. In response to this lacuna, this chapter takes an exploratory lens to the subject of sexuality and the mining industry. We approach the task from the perspective that the mining industry is gendered as masculine. That is, definitions of mining mobilize around masculinized notions of physicality, technical competence with machinery, and strength, as well as emphasize the harshness and dirtiness of the work (Mayes and Pini 2010).

A data mining approach for fault diagnosis: An application of anomaly detection algorithm

Rolling-element bearing failures are the most frequent problems in rotating machinery, which can be catastrophic and cause major downtime. Hence, providing advance failure warning and precise fault detection in such components are pivotal and cost-effective. The vast majority of past research has focused on signal processing and spectral analysis for fault diagnostics in rotating components. In this study, a data mining approach using a machine learning technique called anomaly detection (AD) is presented. This method employs classification techniques to discriminate between defect examples. Two features, kurtosis and Non-Gaussianity Score (NGS), are extracted to develop anomaly detection algorithms. The performance of the developed algorithms was examined through real data from a test to failure bearing. Finally, the application of anomaly detection is compared with one of the popular methods called Support Vector Machine (SVM) to investigate the sensitivity and accuracy of this approach and its ability to detect the anomalies in early stages.

TA-RE: An Exchange Language for Mining Software Repositories

Software repositories have been getting a lot of attention from researchers in recent years. In order to analyze software repositories, it is necessary to first extract raw data from the version control and problem tracking systems. This poses two challenges: (1) extraction requires a non-trivial effort, and (2) the results depend on the heuristics used during extraction. These challenges burden researchers that are new to the community and make it difficult to benchmark software repository mining since it is almost impossible to reproduce experiments done by another team. In this paper we present the TA-RE corpus. TA-RE collects extracted data from software repositories in order to build a collection of projects that will simplify extraction process. Additionally the collection can be used for benchmarking. As the first step we propose an exchange language capable of making sharing and reusing data as simple as possible.

Efficient management of available technologies. Aplication to mining operations for industry supplies

Nowadays, processing Industry Sector is going through a series of changes, including right management and reduction of environmental affections. Any productive process which looks for sustainable management is incomplete if Cycle of Life of mineral resources sustainability is not taken into account. Raw materials for manufacturing are provided by mineral resources extraction processes, such as copper, aluminum, iron, gold, silver, silicon, titanium? Those elements are necessary for Mankind development and are obtained from the Earth through mineral extractive processes. Mineral extraction processes are operations which must take care about the environmental consequences. Extraction of huge volumes of rock for their transformation into raw materials for industry must be optimized to reduce ecological cost of the final product as l was possible. Reducing the ecological balance on a global scale has no sense to design an efficient manufacturing in secondary industry (transformation), if in first steps of the supply chain (extraction) impact exceeds the savings of resources in successive phases. Mining operations size suggests that it is an environmental aggressive activity, but precisely because of its great impact must be the first element to be considered. That idea implies that a new concept born: Reduce economical and environmental cost This work aims to make a reflection on the parameters that can be modified to reduce the energy cost of the process without an increasing in operational costs and always ensuring the same production capacity. That means minimize economic and environmental cost at same time. An efficient design of mining operation which has taken into account that idea does not implies an increasing of the operating cost. To get this objective is necessary to think in global operation view to make that all departments involved have common guidelines which make you think in the optimization of global energy costs. Sometimes a single operational cost must be increased to reduce global cost. This work makes a review through different design parameters of surface mining setting some key performance indicators (KPIs) which are estimated from an efficient point of view. Those KPIs can be included by HQE Policies as global indicators. The new concept developed is that a new criteria has to be applied in company policies: improve management, improving OPERATIONAL efficiency. That means, that is better to use current resources properly (machinery, equipment,?) than to replace them with new things but not used correctly. As a conclusion, through an efficient management of current technologies in each extractive operation an important reduction of the energy can be achieved looking at downstream in the process. That implies a lower energetic cost in the whole cycle of life in manufactured product.

Open business intelligence: on the importance of data quality awareness in user-friendly data mining

Citizens demand more and more data for making decisions in their daily life. Therefore, mechanisms that allow citizens to understand and analyze linked open data (LOD) in a user-friendly manner are highly required. To this aim, the concept of Open Business Intelligence (OpenBI) is introduced in this position paper. OpenBI facilitates non-expert users to (i) analyze and visualize LOD, thus generating actionable information by means of reporting, OLAP analysis, dashboards or data mining; and to (ii) share the new acquired information as LOD to be reused by anyone. One of the most challenging issues of OpenBI is related to data mining, since non-experts (as citizens) need guidance during preprocessing and application of mining algorithms due to the complexity of the mining process and the low quality of the data sources. This is even worst when dealing with LOD, not only because of the different kind of links among data, but also because of its high dimensionality. As a consequence, in this position paper we advocate that data mining for OpenBI requires data quality-aware mechanisms for guiding non-expert users in obtaining and sharing the most reliable knowledge from the available LOD.

The Elements of mining engineering [microform] /

Includes indexes.