Event cube : another perspective on business processes

In this paper the so-called Event Cube is introduced, a multidimensional data structure that can hold information about all business dimensions. Like the data cubes of online analytic processing (OLAP) systems, the Event Cube can be used to improve the business analysis quality by providing immediate results under different levels of abstraction. An exploratory analysis of the application of process mining on multidimensional process data is the focus of this paper. The feasibility and potential of this approach is demonstrated through some practical examples

Flexible heuristics miner (FHM)

One of the aims of process mining is to retrieve a process model from a given event log. However, current techniques have problems when mining processes that contain non-trivial constructs, processes that are low structured and/or dealing with the presence of noise in the event logs. To overcome these problems, a new process representation language (i.e. augmented Causal nets) is presented in combination with an accompanying process mining algorithm. The most signficant property of the new representation language is in the way the semantics of splits and joins are represented; by using so-called split/join frequency tables. This result in easy to understand process models even in the case of non-trivial constructs, low structured domains and the presence of noise. The new process representation language and mining technique can also be used for conformance checking; to indicate if all the behavior in the event log is also represented in the process model and if there is extra behavior in the process model not in the event log. This paper explains the new process representation language and how the mining algorithm works. The algorithm is implemented as a plug-in in the ProM framework. An illustrative example with noise and a real life log of a complex and low structured process are used to explicate the presented approach

Ranking gradients in multi-dimensional spaces

Business organizations must pay attention to interesting changes in customer behavior in order to anticipate their needs and act accordingly with appropriated business actions. Tracking customer's commercial paths through the products they are interested in is an essential technique to improve business and increase customer satisfaction. Data warehousing (DW) allows us to do so, giving the basic means to record every customer transaction based on the different business strategies established. Although managing such huge amounts of records may imply business advantage, its exploration, especially in a multi-dimensional space (MDS), is a nontrivial task. The more dimensions we want to explore, the more are the computational costs involved in multi-dimensional data analysis (MDA). To make MDA practical in real world business problems, DW researchers have been working on combining data cubing and mining techniques to detect interesting changes in MDS. Such changes can also be detected through gradient queries. While those studies have provided the basis for future research in MDA, just few of them points to preference query selection in MDS. Thus, not only the exploration of changes in MDS is an essential task, but also even more important is ranking most interesting gradients. In this chapter, the authors investigate how to mine and rank the most interesting changes in a MDS applying a TOP-K gradient strategy. Additionally, the authors also propose a gradient-based cubing method to evaluate interesting gradient regions in MDS. So, the challenge is to find maximum gradient regions (MGRs) that maximize the task of raking gradients in a MDS. The authors' evaluation study demonstrates that the proposed method presents a promising strategy for ranking gradients in MDS.(undefined)info:eu-repo/semantics/publishedVersio

Detecting abnormal patterns in call graphs based on the aggregation of relevant vertex measures

Graphs are a very important abstraction to model complex structures and respective interactions, with a broad range of applica- tions including web analysis, telecommunications, chemical informatics and bioinformatics. In this work we are interested in the application of graph mining to identify abnormal behavior patterns from telecom Call Detail Records (CDRs). Such behaviors could also be used to model essential business tasks in telecom, for example churning, fraud, or mar- keting strategies, where the number of customers is typically quite large. Therefore, it is important to rank the most interesting patterns for fur- ther analysis. We propose a vertex relevant ranking score as a unified measure for focusing the search of abnormal patterns in weighted call graphs based on CDRs. Classical graph-vertex measures usually expose a quantitative perspective of vertices in telecom call graphs. We aggre- gate wellknown vertex measures for handling attribute-based information usually provided by CDRs. Experimental evaluation carried out with real data streams, from a local mobile telecom company, showed us the fea- sibility of the proposed strategy.(undefined

Mining Top-K multidimensional gradients

Several business applications such as marketing basket analysis, clickstream analysis, fraud detection and churning migration analysis demand gradient data analysis. By employing gradient data analysis one is able to identify trends, outliers and answering "what-if" questions over large databases. Gradient queries were first introduced by Imielinski et al [1] as the cubegrade problem. The main idea is to detect interesting changes in a multidimensional space (MDS). Thus, changes in a set of measures (aggregates) are associated with changes in sector characteristics (dimensions). MDS contains a huge number of cells which poses great challenge for mining gradient cells on a useful time. Dong et al [2] have proposed gradient constraints to smooth the computational costs involved in such queries. Even by using such constraints on large databases, the number of interesting cases to evaluate is still large. In this work, we are interested to explore best cases (Top-K cells) of interesting multidimensional gradients. There several studies on Top-K queries, but preference queries with multidimensional selection were introduced quite recently by Dong et al [9]. Furthermore, traditional Top-K methods work well in presence of convex functions (gradients are non-convex ones). We have revisited iceberg cubing for complex measures, since it is the basis for mining gradient cells. We also propose a gradient-based cubing strategy to evaluate interesting gradient regions in MDS. Thus, the main challenge is to find maximum gradient regions (MGRs) that maximize the task of mining Top-K gradient cells. Our performance study indicates that our strategy is effective on finding the most interesting gradients in multidimensional space

Discovering telecom fraud situations through mining anomalous behavior patterns

In this paper we tackle the problem of superimposed fraud detection in telecommunication systems. We propose two anomaly detection methods based on the concept of signatures. The first method relies on a signature deviation-based approach while the second on a dynamic clustering analysis. Experiments carried out with real data, voice call records from an entire week, corresponding to approximately 2.5 millions of CDRs and 700 thousand of signatures processed per day, allowed us to detect several anomalous situations. The frauds analysts provide us a small list of 12 customers for whom a fraudulent behavior was detected during this week. Thus, 9 and 11 fraud situations were discovered from each method respectively. Preliminary results and discussion with fraud analysts has already proved that our methods are a valuable tool to assist them in fraud detection

Variação do teor de umidade e da densidade básica na madeira de sete espécies de eucalipto Variation of the moisture content and specific gravity in the wood of seven eucalypt species

As variações de umidade e da densidade do lenho das árvores são as principais causas dos defeitos de secagem, como o empenamento e fendilhamento das peças de madeira. Os tipos de madeira presentes em um tronco estão relacionados com as variações dessas duas importantes propriedades físicas. Os gradientes de umidade e da densidade da madeira de sete espécies de eucalipto foram avaliados nas direções radial e longitudinal do tronco de árvores recém-abatidas. Os resultados apontaram uma maior homogeneidade de distribuição de umidade dentro das árvores de E. paniculata e E. citriodora, indicada pelos coeficientes de variação e desvio-padrão. O diferencial de umidade da madeira nas regiões internas do tronco de E. paniculata e E. citriodora foi de 20% e de E. urophylla e E. grandis, de 80%. A densidade básica da madeira aumentou na direção radial do tronco, e cada espécie de eucalipto apresentou um modelo de variação.<br>The occurrence of high moisture and density gradients inside of the tree is related with drying defects, especially those characterized by warp and split of the wood. The unequal behavior of wood pieces due to variation of these two properties can be attributed to differences within the wood that constitutes the log. Considering the importance of knowing the moisture and density gradients in eucalyptus trees, this work had as objective evaluating the variations of the moisture content and specific gravity in the radial and longitudinal directions of the trees of seven eucalypts species. Based on the results concerning the moisture distribution inside the tree, there was a great homogeneity in the species of E. paniculata and E. citriodora, sustained by the low values of variation coefficient and standard deviation. In these species, the moisture differential among the inner and outer parts of the log rarely reached 20%, while for the logs of E. urophylla and E. grandis, this diferencial reached 80%. In spite of the specific gravity general increased in the direction pith-to-bark, each species presented a specific profile of variation, also with variants for each height of the log

Event cube : another perspective on business processes

In this paper the so-called Event Cube is introduced, a multidimensional data structure that can hold information about all business dimensions. Like the data cubes of online analytic processing (OLAP) systems, the Event Cube can be used to improve the business analysis quality by providing immediate results under different levels of abstraction. An exploratory analysis of the application of process mining on multidimensional process data is the focus of this paper. The feasibility and potential of this approach is demonstrated through some practical examples