A process mining-based investigation of adverse events in care processes

This paper proposes the Clinical Pathway Analysis Method (CPAM) approach that enables the extraction of valuable organisational and medical information on past clinical pathway executions from the event logs of healthcare information systems. The method deals with the complexity of real-world clinical pathways by introducing a perspective-based segmentation of the date-stamped event log. CPAM enables the clinical pathway analyst to effectively and efficiently acquire a profound insight into the clinical pathways. By comparing the specific medical conditions of patients with the factors used for characterising the different clinical pathway variants, the medical expert can identify the best therapeutic option. Process mining-based analytics enables the acquisition of valuable insights into clinical pathways, based on the complete audit traces of previous clinical pathway instances. Additionally, the methodology is suited to assess guideline compliance and analyse adverse events. Finally, the methodology provides support for eliciting tacit knowledge and providing treatment selection assistance.

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.

The case for “n«all”: Why the Big Data revolution will probably happen differently in the mining sector

Big Data and predictive analytics have received significant attention from the media and academic literature throughout the past few years, and it is likely that these emerging technologies will materially impact the mining sector. This short communication argues, however, that these technological forces will probably unfold differently in the mining industry than they have in many other sectors because of significant differences in the marginal cost of data capture and storage. To this end, we offer a brief overview of what Big Data and predictive analytics are, and explain how they are bringing about changes in a broad range of sectors. We discuss the “N=all” approach to data collection being promoted by many consultants and technology vendors in the marketplace but, by considering the economic and technical realities of data acquisition and storage, we then explain why a “n « all” data collection strategy probably makes more sense for the mining sector. Finally, towards shaping the industry’s policies with regards to technology-related investments in this area, we conclude by putting forward a conceptual model for leveraging Big Data tools and analytical techniques that is a more appropriate fit for the mining sector.

Enhancement of relevant features for text mining

With the explosion of information resources, there is an imminent need to understand interesting text features or topics in massive text information. This thesis proposes a theoretical model to accurately weight specific text features, such as patterns and n-grams. The proposed model achieves impressive performance in two data collections, Reuters Corpus Volume 1 (RCV1) and Reuters 21578.

Using global formulary apportionment for international profit allocation: The case of Indonesia's mining industry

My thesis examined an alternative approach, referred to as the unitary taxation approach to the allocation of profit, which arises from the notion that as a multinational group exists as a single economic entity, it should be taxed as one taxable unit. The plausibility of a unitary taxation regime achieving international acceptance and agreement is highly contestable due to its implementation issues, and economic and political feasibility. Using a case-study approach focusing on Freeport-McMoRan and Rio Tinto's mining operations in Indonesia, this thesis compares both tax regimes against the criteria for a good tax system - equity, efficiency, neutrality and simplicity. This thesis evaluates key issues that arise when implementing a unitary taxation approach with formulary apportionment based on the context of mining multinational firms in Indonesia.

Business process performance mining with staged process flows

Existing process mining techniques provide summary views of the overall process performance over a period of time, allowing analysts to identify bottlenecks and associated performance issues. However, these tools are not de- signed to help analysts understand how bottlenecks form and dissolve over time nor how the formation and dissolution of bottlenecks – and associated fluctua- tions in demand and capacity – affect the overall process performance. This paper presents an approach to analyze the evolution of process performance via a notion of Staged Process Flow (SPF). An SPF abstracts a business process as a series of queues corresponding to stages. The paper defines a number of stage character- istics and visualizations that collectively allow process performance evolution to be analyzed from multiple perspectives. The approach has been implemented in the ProM process mining framework. The paper demonstrates the advantages of the SPF approach over state-of-the-art process performance mining tools using two real-life event logs publicly available.

Tree-ring based hydroclimate reconstruction over northern Vietnam from Fokienia hodginsii: eighteenth century mega-drought and tropical Pacific influence

We present here the first statistically calibrated and verified tree-ring reconstruction of climate from continental Southeast Asia.The reconstructed variable is March-May (MAM) Palmer Drought Severity Index (PDSI) based on ring widths from 22 trees (42 radial cores) of rare and long-lived conifer, Fokienia hodginsii (Po Mu as locally called) from northern Vietnam. This is the first published tree ring chronology from Vietnam as well as the first for this species. Spanning 535 years, this is the longest cross-dated tree-ring series yet produced from continental Southeast Asia. Response analysis revealed that the annual growth of Fokienia at this site was mostly governed by soil moisture in the pre-monsoon season. The reconstruction passed the calibration-verification tests commonly used in dendroclimatology, and revealed two prominent periods of drought in the mid-eighteenth and late-nineteenth enturies. The former lasted nearly 30 years and was concurrent with a similar drought over northwestern Thailand inferred from teak rings, suggesting a ``mega-drought'' extending across Indochina in the eighteenth century. Both of our reconstructed droughts are consistent with the periods of warm sea surface temperature (SST)anomalies in the tropical Pacific. Spatial correlation analyses with global SST indicated that ENSO-like anomalies might play a role in modulating droughts over the region, with El Nio (warm) phases resulting in reduced rainfall. However, significant correlation was also seen with SST over the Indian Ocean and the north Pacific,suggesting that ENSO is not the only factor affecting the climate of the area. Spectral analyses revealed significant peaks in the range of 53.9-78.8 years as well as in the ENSO-variability range of 2.0 to 3.2 years.

A mega-analysis of genome-wide association studies for major depressive disorder

Prior genome-wide association studies (GWAS) of major depressive disorder (MDD) have met with limited success. We sought to increase statistical power to detect disease loci by conducting a GWAS mega-analysis for MDD. In the MDD discovery phase, we analyzed more than 1.2 million autosomal and X chromosome single-nucleotide polymorphisms (SNPs) in 18 759 independent and unrelated subjects of recent European ancestry (9240 MDD cases and 9519 controls). In the MDD replication phase, we evaluated 554 SNPs in independent samples (6783 MDD cases and 50 695 controls). We also conducted a cross-disorder meta-analysis using 819 autosomal SNPs with P<0.0001 for either MDD or the Psychiatric GWAS Consortium bipolar disorder (BIP) mega-analysis (9238 MDD cases/8039 controls and 6998 BIP cases/7775 controls). No SNPs achieved genome-wide significance in the MDD discovery phase, the MDD replication phase or in pre-planned secondary analyses (by sex, recurrent MDD, recurrent early-onset MDD, age of onset, pre-pubertal onset MDD or typical-like MDD from a latent class analyses of the MDD criteria). In the MDD-bipolar cross-disorder analysis, 15 SNPs exceeded genome-wide significance (P<5 x 10(-8)), and all were in a 248 kb interval of high LD on 3p21.1 (chr3:52 425 083-53 822 102, minimum P=5.9 x 10(-9) at rs2535629). Although this is the largest genome-wide analysis of MDD yet conducted, its high prevalence means that the sample is still underpowered to detect genetic effects typical for complex traits. Therefore, we were unable to identify robust and replicable findings. We discuss what this means for genetic research for MDD. The 3p21.1 MDD-BIP finding should be interpreted with caution as the most significant SNP did not replicate in MDD samples, and genotyping in independent samples will be needed to resolve its status.

Cat's claw creeper leaf-mining jewel beetle Hylaeogena jureceki Obenberger (Coleoptera: Buprestidae), a host-specific biological control agent for Dolichandra unguis-cati (Bignoniaceae) in Australia

Cat's claw creeper, Dolichandra unguis-cati (L.) L.G. Lohman (syn: Macfadyena unguis-cati (L.) A.H. Gentry) (Bignoniaceae), a major environmental weed in Queensland and New South Wales, is a Weed of National Significance and an approved target for biological control. A leaf-mining jewel beetle, Hylaeogena jureceki Obenberger (Coleoptera: Buprestidae), first collected in 2002 from D. unguis-cati in Brazil and Argentina, was imported from South Africa into a quarantine facility in Brisbane in 2009 for host-specificity testing. H. jureceki adults chew holes in leaves and lay eggs on leaf margins and the emerging larvae mine within the leaves of D. unguis-cati. The generation time (egg to adult) of H. jureceki under quarantine conditions was 55.4 ± 0.2 days. Host-specificity trials conducted in Australia on 38 plant species from 11 families supplement and support South African studies which indicated that H. jureceki is highly host-specific and does not pose a risk to any non-target plant species in Australia. In no-choice tests, adults survived significantly longer (>32 weeks) on D. unguis-cati than on non-target test plant species (<3 weeks). Oviposition occurred on D. unguis-cati and one non-target test plant species, Citharexylum spinosum (Verbenaceae), but no larval development occurred on the latter species. In choice tests involving D. unguis-cati, C. spinosum and Avicennia marina (Avicenniaceae), feeding and oviposition were evident only on D. unguis-cati. The insect was approved for field release in Australia in May 2012.

Precipitation-induced runoff and leaching from milled peat mining mires by peat types : a comparative method for estimating the loading of water bodies during peat pruduction

Precipitation-induced runoff and leaching from milled peat mining mires by peat types: a comparative method for estimating the loading of water bodies during peat production. This research project in environmental geology has arisen out of an observed need to be able to predict more accurately the loading of watercourses with detrimental organic substances and nutrients from already existing and planned peat production areas, since the authorities capacity for insisting on such predictions covering the whole duration of peat production in connection with evaluations of environmental impact is at present highly limited. National and international decisions regarding monitoring of the condition of watercourses and their improvement and restoration require more sophisticated evaluation methods in order to be able to forecast watercourse loading and its environmental impacts at the stage of land-use planning and preparations for peat production.The present project thus set out from the premise that it would be possible on the basis of existing mire and peat data properties to construct estimates for the typical loading from production mires over the whole duration of their exploitation. Finland has some 10 million hectares of peatland, accounting for almost a third of its total area. Macroclimatic conditions have varied in the course of the Holocene growth and development of this peatland, and with them the habitats of the peat-forming plants. Temperatures and moisture conditions have played a significant role in determining the dominant species of mire plants growing there at any particular time, the resulting mire types and the accumulation and deposition of plant remains to form the peat. The above climatic, environmental and mire development factors, together with ditching, have contributed, and continue to contribute, to the existence of peat horizons that differ in their physical and chemical properties, leading to differences in material transport between peatlands in a natural state and mires that have been ditched or prepared for forestry and peat production. Watercourse loading from the ditching of mires or their use for peat production can have detrimental effects on river and lake environments and their recreational use, especially where oxygen-consuming organic solids and soluble organic substances and nutrients are concerned. It has not previously been possible, however, to estimate in advance the watercourse loading likely to arise from ditching and peat production on the basis of the characteristics of the peat in a mire, although earlier observations have indicated that watercourse loading from peat production can vary greatly and it has been suggested that differences in peat properties may be of significance in this. Sprinkling is used here in combination with simulations of conditions in a milled peat production area to determine the influence of the physical and chemical properties of milled peats in production mires on surface runoff into the drainage ditches and the concentrations of material in the runoff water. Sprinkling and extraction experiments were carried out on 25 samples of milled Carex (C) and Sphagnum (S) peat of humification grades H 2.5 8.5 with moisture content in the range 23.4 89% on commencement of the first sprinkling, which was followed by a second sprinkling 24 hours later. The water retention capacity of the peat was best, and surface runoff lowest, with Sphagnum and Carex peat samples of humification grades H 2.5 6 in the moisture content class 56 75%. On account of the hydrophobicity of dry peat, runoff increased in a fairly regular manner with drying of the sample from 55% to 24 30%. Runoff from the samples with an original moisture content over 55% increased by 63% in the second round of sprinkling relative to the first, as they had practically reached saturation point on the first occasion, while those with an original moisture content below 55% retained their high runoff in the second round, due to continued hydrophobicity. The well-humified samples (H 6.5 8.5) with a moisture content over 80% showed a low water retention capacity and high runoff in both rounds of sprinkling. Loading of the runoff water with suspended solids, total phosphorus and total nitrogen, and also the chemical oxygen demand (CODMn O2), varied greatly in the sprinkling experiment, depending on the peat type and degree of humification, but concentrations of the same substances in the two sprinklings were closely or moderately closely correlated and these correlations were significant. The concentrations of suspended solids in the runoff water observed in the simulations of a peat production area and the direct surface runoff from it into the drainage ditch system in response to rain (sprinkling intensity 1.27 mm/min) varied c. 60-fold between the degrees of humification in the case of the Carex peats and c. 150-fold for the Sphagnum peats, while chemical oxygen demand varied c. 30-fold and c. 50-fold, respectively, total phosphorus c. 60-fold and c. 66-fold, total nitrogen c. 65-fold and c. 195-fold and ammonium nitrogen c. 90-fold and c. 30-fold. The increases in concentrations in the runoff water were very closely correlated with increases in humification of the peat. The correlations of the concentrations measured in extraction experiments (48 h) with peat type and degree of humification corresponded to those observed in the sprinkler experiments. The resulting figures for the surface runoff from a peat production area into the drainage ditches simulated by means of sprinkling and material concentrations in the runoff water were combined with statistics on the mean extent of daily rainfall (0 67 mm) during the frost-free period of the year (May October) over an observation period of 30 years to yield typical annual loading figures (kg/ha) for suspended solids (SS), chemical oxygen demand of organic matter (CODmn O2), total phosphorus (tot. P) and total nitrogen (tot. N) entering the ditches with respect to milled Carex (C) and Sphagnum (S) peats of humification grades H 2.5 8.5. In order to calculate the loading of drainage ditches from a milled peat production mire with the aid of these annual comparative values (in kg/ha), information is required on the properties of the intended production mire and its peat. Once data are available on the area of the mire, its peat depth, peat types and their degrees of humification, dry matter content, calorific value and corresponding energy content, it is possible to produce mutually comparable estimates for individual mires with respect to the annual loading of the drainage ditch system and the surrounding watercourse for the whole service life of the production area, the duration of this service life, determinations of energy content and the amount of loading per unit of energy generated (kg/MWh). In the 8 mires in the Köyhäjoki basin, Central Ostrobothnia, taken as an example, the loading of suspended solids (SS) in the drainage ditch networks calculated on the basis of the typical values obtained here and existing mire and peat data and expressed per unit of energy generated varied between the mires and horizons in the range 0.9 16.5 kg/MWh. One of the aims of this work was to develop means of making better use of existing mire and peat data and the results of corings and other field investigations. In this respect combination of the typical loading values (kg/ha) obtained here for S, SC, CS and C peats and the various degrees of humification (H 2.5 8.5) with the above mire and peat data by means of a computer program for the acquisition and handling of such data would enable all the information currently available and that deposited in the system in the future to be used for defining watercourse loading estimates for mires and comparing them with the corresponding estimates of energy content. The intention behind this work has been to respond to the challenge facing the energy generation industry to find larger peat production areas that exert less loading on the environment and to that facing the environmental authorities to improve the means available for estimating watercourse loading from peat production and its environmental impacts in advance. The results conform well to the initial hypothesis and to the goals laid down for the research and should enable watercourse loading from existing and planned peat production to be evaluated better in the future and the resulting impacts to be taken into account when planning land use and energy generation. The advance loading information available in this way would be of value in the selection of individual peat production areas, the planning of their exploitation, the introduction of water protection measures and the planning of loading inspections, in order to achieve controlled peat production that pays due attention to environmental considerations.

Data mining for telecommunications network log analysis

Telecommunications network management is based on huge amounts of data that are continuously collected from elements and devices from all around the network. The data is monitored and analysed to provide information for decision making in all operation functions. Knowledge discovery and data mining methods can support fast-pace decision making in network operations. In this thesis, I analyse decision making on different levels of network operations. I identify the requirements decision-making sets for knowledge discovery and data mining tools and methods, and I study resources that are available to them. I then propose two methods for augmenting and applying frequent sets to support everyday decision making. The proposed methods are Comprehensive Log Compression for log data summarisation and Queryable Log Compression for semantic compression of log data. Finally I suggest a model for a continuous knowledge discovery process and outline how it can be implemented and integrated to the existing network operations infrastructure.

Mining Sequential Data – in Search of Segmental Structures

Segmentation is a data mining technique yielding simplified representations of sequences of ordered points. A sequence is divided into some number of homogeneous blocks, and all points within a segment are described by a single value. The focus in this thesis is on piecewise-constant segments, where the most likely description for each segment and the most likely segmentation into some number of blocks can be computed efficiently. Representing sequences as segmentations is useful in, e.g., storage and indexing tasks in sequence databases, and segmentation can be used as a tool in learning about the structure of a given sequence. The discussion in this thesis begins with basic questions related to segmentation analysis, such as choosing the number of segments, and evaluating the obtained segmentations. Standard model selection techniques are shown to perform well for the sequence segmentation task. Segmentation evaluation is proposed with respect to a known segmentation structure. Applying segmentation on certain features of a sequence is shown to yield segmentations that are significantly close to the known underlying structure. Two extensions to the basic segmentation framework are introduced: unimodal segmentation and basis segmentation. The former is concerned with segmentations where the segment descriptions first increase and then decrease, and the latter with the interplay between different dimensions and segments in the sequence. These problems are formally defined and algorithms for solving them are provided and analyzed. Practical applications for segmentation techniques include time series and data stream analysis, text analysis, and biological sequence analysis. In this thesis segmentation applications are demonstrated in analyzing genomic sequences.