Predicting deadline transgressions using event logs

Effective risk management is crucial for any organisation. One of its key steps is risk identification, but few tools exist to support this process. Here we present a method for the automatic discovery of a particular type of process-related risk, the danger of deadline transgressions or overruns, based on the analysis of event logs. We define a set of time-related process risk indicators, i.e., patterns observable in event logs that highlight the likelihood of an overrun, and then show how instances of these patterns can be identified automatically using statistical principles. To demonstrate its feasibility, the approach has been implemented as a plug-in module to the process mining framework ProM and tested using an event log from a Dutch financial institution

Customer profile classification using transactional data

Customer profiles are by definition made up of factual and transactional data. It is often the case that due to reasons such as high cost of data acquisition and/or protection, only the transactional data are available for data mining operations. Transactional data, however, tend to be highly sparse and skewed due to a large proportion of customers engaging in very few transactions. This can result in a bias in the prediction accuracy of classifiers built using them towards the larger proportion of customers with fewer transactions. This paper investigates an approach for accurately and confidently grouping and classifying customers in bins on the basis of the number of their transactions. The experiments we conducted on a highly sparse and skewed real-world transactional data show that our proposed approach can be used to identify a critical point at which customer profiles can be more confidently distinguished

Discrete/finite element modelling of rock cutting with a TBM disc cutter

The final publication is available at Springer via http://dx.doi.org/10.1007/s00603-016-1133-7This paper presents advanced computer simulation of rock cutting process typical for excavation works in civil engineering. Theoretical formulation of the hybrid discrete/finite element model has been presented. The discrete and finite element methods have been used in different subdomains of a rock sample according to expected material behaviour, the part which is fractured and damaged during cutting is discretized with the discrete elements while the other part is treated as a continuous body and it is modelled using the finite element method. In this way, an optimum model is created, enabling a proper representation of the physical phenomena during cutting and efficient numerical computation. The model has been applied to simulation of the laboratory test of rock cutting with a single TBM (tunnel boring machine) disc cutter. The micromechanical parameters have been determined using the dimensionless relationships between micro- and macroscopic parameters. A number of numerical simulations of the LCM test in the unrelieved and relieved cutting modes have been performed. Numerical results have been compared with available data from in-situ measurements in a real TBM as well as with the theoretical predictions showing quite a good agreement. The numerical model has provided a new insight into the cutting mechanism enabling us to investigate the stress and pressure distribution at the tool–rock interaction. Sensitivity analysis of rock cutting performed for different parameters including disc geometry, cutting velocity, disc penetration and spacing has shown that the presented numerical model is a suitable tool for the design and optimization of rock cutting process.Peer ReviewedPostprint (published version

Adaptive Evolutionary Clustering

In many practical applications of clustering, the objects to be clustered evolve over time, and a clustering result is desired at each time step. In such applications, evolutionary clustering typically outperforms traditional static clustering by producing clustering results that reflect long-term trends while being robust to short-term variations. Several evolutionary clustering algorithms have recently been proposed, often by adding a temporal smoothness penalty to the cost function of a static clustering method. In this paper, we introduce a different approach to evolutionary clustering by accurately tracking the time-varying proximities between objects followed by static clustering. We present an evolutionary clustering framework that adaptively estimates the optimal smoothing parameter using shrinkage estimation, a statistical approach that improves a naive estimate using additional information. The proposed framework can be used to extend a variety of static clustering algorithms, including hierarchical, k-means, and spectral clustering, into evolutionary clustering algorithms. Experiments on synthetic and real data sets indicate that the proposed framework outperforms static clustering and existing evolutionary clustering algorithms in many scenarios.Comment: To appear in Data Mining and Knowledge Discovery, MATLAB toolbox available at http://tbayes.eecs.umich.edu/xukevin/affec

Haul Truck Tires Recycling

The disposal of large Off-the-Road (OTR) tires is an increasingly important concern. These tires can weigh up to 8,450 pounds with an overall diameter and width of approximately 140.7 inches and 45.1 inches respectively. OTR tires are used for mining vehicles such as haul trucks, wheel loaders, backhoes, graders, and trenchers.[1] These new tires cost between $38,000 and$50,000 each, depending on multiple factors including oil prices and the cyclical nature of the industry. Haul trucks contain six tires per vehicle, and mines replace these tires around every 9-12 months.[2] Statistics regarding discarded OTR tires are not provided by the industry as they are for other types of tires. Thus, it is difficult to approximate the number and location of waste OTR tires not only in individual states, but in the U.S. in general.[3] Currently, Minnesota and Arizona are the only states that place regulations and fees on OTR tires. However, Minnesota is the only state that actually tracks them.[3] The Rubber Manufacturers Association (RMA) roughly estimates that OTR tires account for 1% of scrap tires by number and 15% by weight. When the tires are replaced, the old tires can be discarded with the waste rock in stockpiles at the mining site but more often are landfilled without documentation by an appropriate agency due to lack of federal regulations. Their low density and hollow centers cause them to float to the top of landfills, disrupting the compactness.[4] Also, tires have a heat content 20-40% greater than that of coal which can be very dangerous on the rare occasions that tires catch fire in stockpiles.[5] Furthermore, burning tires release hazardous substances including pyrolytic oil, ash, and smoke, which contain carcinogens, heavy metals, and other toxic compounds.[6] Due to the large size of OTR tires, there are few facilities that can accommodate their recycling.[3] This leads to increased costs in transporting them to such sites. Transportation costs for a tire taken out of service can be up to $1000. In addition to the freight costs, recycling an OTR tire can cost up to$1500 because of their rugged construction compared to passenger tires which cost around 1 to recycle.[3] In response to the waste OTR tire problem, the Ball Hogs from the University of Arkansas have designed a solution that recycles OTR tires by using old tires as liners in ball mills for hard rock mines. Ball mills are large cylindrical vessels consisting of an outer shell, an inside liner and a load of metal balls. A motor turns the ball mill using a transmission system causing the metal balls to move in a cascading motion to grind the material fed into the ball mill. Ball mills require liners that are constructed from materials such as steel or rubber. For a 30 ft long ball mill with a Task #2 5 University of Arkansas 20 ft diameter, a hard rubber liner reinforced with steel can cost 150,000. These liners are replaced at least once a year, creating a substantial upkeep cost for these ball mills. Metal mines in Bolivia are already using tractor tires to line many ball mills. This technique has been effective for over twenty years. The high import costs of new liners and the low cost of labor has led many Bolivian metal processors to use truck and tractor tires as liners in their ball mills. This construction normally occurs on site using tools like handsaws, drills, torches and knives to cut up tires and manpower to mount these tire-made liners onto mills. However, this is not always the case in the U.S. where labor costs are much higher and the mills are generally larger. Many mines in the U.S. do not have the means to fabricate and install these liners on site; therefore, a third-party solution is proposed that will take a mine’s discarded tires and make ball mill liners out of them. The Ball Hogs’ solution provides an environmentally and economically feasible process of increasing the life of OTR tires beyond their typical use. This alternative would utilize the engineering and technology that makes these tires strong enough to hold a 400 ton truck. Mining companies would save yearly an average of $70,000 per ball mill liner replacement, and over 780,000 kg of CO2 per liner. Furthermore, mining companies would earn positive PR, goodwill, and tax breaks. We recommend all mining companies use their OTR tire treads as ball mill liners

Clustering and Community Detection in Directed Networks: A Survey

Networks (or graphs) appear as dominant structures in diverse domains, including sociology, biology, neuroscience and computer science. In most of the aforementioned cases graphs are directed - in the sense that there is directionality on the edges, making the semantics of the edges non symmetric. An interesting feature that real networks present is the clustering or community structure property, under which the graph topology is organized into modules commonly called communities or clusters. The essence here is that nodes of the same community are highly similar while on the contrary, nodes across communities present low similarity. Revealing the underlying community structure of directed complex networks has become a crucial and interdisciplinary topic with a plethora of applications. Therefore, naturally there is a recent wealth of research production in the area of mining directed graphs - with clustering being the primary method and tool for community detection and evaluation. The goal of this paper is to offer an in-depth review of the methods presented so far for clustering directed networks along with the relevant necessary methodological background and also related applications. The survey commences by offering a concise review of the fundamental concepts and methodological base on which graph clustering algorithms capitalize on. Then we present the relevant work along two orthogonal classifications. The first one is mostly concerned with the methodological principles of the clustering algorithms, while the second one approaches the methods from the viewpoint regarding the properties of a good cluster in a directed network. Further, we present methods and metrics for evaluating graph clustering results, demonstrate interesting application domains and provide promising future research directions.Comment: 86 pages, 17 figures. Physics Reports Journal (To Appear