Detecting drift from event streams of unpredictable business processes

Existing business process drift detection methods do not work with event streams. As such, they are designed to detect inter-trace drifts only, i.e. drifts that occur between complete process executions (traces), as recorded in event logs. However, process drift may also occur during the execution of a process, and may impact ongoing executions. Existing methods either do not detect such intra-trace drifts, or detect them with a long delay. Moreover, they do not perform well with unpredictable processes, i.e. processes whose logs exhibit a high number of distinct executions to the total number of executions. We address these two issues by proposing a fully automated and scalable method for online detection of process drift from event streams. We perform statistical tests over distributions of behavioral relations between events, as observed in two adjacent windows of adaptive size, sliding along with the stream. An extensive evaluation on synthetic and real-life logs shows that our method is fast and accurate in the detection of typical change patterns, and performs significantly better than the state of the art.

Measurements of Volatile Organic Compounds - from Biogenic Emissions to Concentrations in Ambient Air

Volatile organic compounds (VOCs) affect atmospheric chemistry and thereafter also participate in the climate change in many ways. The long-lived greenhouse gases and tropospheric ozone are the most important radiative forcing components warming the climate, while aerosols are the most important cooling component. VOCs can have warming effects on the climate: they participate in tropospheric ozone formation and compete for oxidants with the greenhouse gases thus, for example, lengthening the atmospheric lifetime of methane. Some VOCs, on the other hand, cool the atmosphere by taking part in the formation of aerosol particles. Some VOCs, in addition, have direct health effects, such as carcinogenic benzene. VOCs are emitted into the atmosphere in various processes. Primary emissions of VOC include biogenic emissions from vegetation, biomass burning and human activities. VOCs are also produced in secondary emissions from the reactions of other organic compounds. Globally, forests are the largest source of VOC entering the atmosphere. This thesis focuses on the measurement results of emissions and concentrations of VOCs in one of the largest vegetation zones in the world, the boreal zone. An automated sampling system was designed and built for continuous VOC concentration and emission measurements with a proton transfer reaction - mass spectrometer (PTR-MS). The system measured one hour at a time in three-hourly cycles: 1) ambient volume mixing-ratios of VOCs in the Scots-pine-dominated boreal forest, 2) VOC fluxes above the canopy, and 3) VOC emissions from Scots pine shoots. In addition to the online PTR-MS measurements, we determined the composition and seasonality of the VOC emissions from a Siberian larch with adsorbent samples and GC-MS analysis. The VOC emissions from Siberian larch were reported for the fist time in the literature. The VOC emissions were 90% monoterpenes (mainly sabinene) and the rest sesquiterpenes (mainly a-farnesene). The normalized monoterpene emission potentials were highest in late summer, rising again in late autumn. The normalized sesquiterpene emission potentials were also highest in late summer, but decreased towards the autumn. The emissions of mono- and sesquiterpenes from the deciduous Siberian larch, as well as the emissions of monoterpenes measured from the evergreen Scots pine, were well described by the temperature-dependent algorithm. In the Scots-pine-dominated forest, canopy-scale emissions of monoterpenes and oxygenated VOCs (OVOCs) were of the same magnitude. Methanol and acetone were the most abundant OVOCs emitted from the forest and also in the ambient air. Annually, methanol and mixing ratios were of the order of 1 ppbv. The monoterpene and sum of isoprene 2-methyl-3-buten-2-ol (MBO) volume mixing-ratios were an order of magnitude lower. The majority of the monoterpene and methanol emissions from the Scots-pinedominated forest were explained by emissions from Scots pine shoots. The VOCs were divided into three classes based on the dynamics of the summer-time concentrations: 1) reactive compounds with local biological, anthropogenic or chemical sources (methanol, acetone, butanol and hexanal), 2) compounds whose emissions are only temperaturedependent (monoterpenes), 3) long-lived compounds (benzene, acetaldehyde). Biogenic VOC (methanol, acetone, isoprene MBO and monoterpene) volume mixing-ratios had clear diurnal patterns during summer. The ambient mixing ratios of other VOCs did not show this behaviour. During winter we did not observe systematical diurnal cycles for any of the VOCs. Different sources, removal processes and turbulent mixing explained the dynamics of the measured mixing-ratios qualitatively. However, quantitative understanding will require longterm emission measurements of the OVOCs and the use of comprehensive chemistry models. Keywords: Hydrocarbons, VOC, fluxes, volume mixing-ratio, boreal forest

Verkkomedian käyttö ja tutkiminen : Iltalehti Online 1995-2001

Marja Heinonen s dissertation Verkkomedian käyttö ja tutkiminen. Iltalehti Online 1995-2001 describes the usage of new internet based news service Iltalehti Online during its first years of existence, 1995-2001. The study focuses on the content of the service and users attitudes towards the new media and its contents. Heinonen has also analyzed and described the research methods that can be used in the research of any new media phenomenon when there is no historical perspective to do the research. Heinonen has created a process model for the research of net medium, which is based on a multidimensional approach. She has chosen an iterative research method inspired by Sudweeks and Simoff s CEDA-methodology in which qualitative and quantitative methods take turns both creating results and new research questions. The dissertation discusses and describes the possibilities of combining several research methods in the study of online news media. On general level it discusses the methodological possibilities of researching a completely new media form when there is no historical perspective. The result of these discussions is in favour for the multidimensional methods. The empiric research was built around three cases of Iltalehti Online among its users: log analysis 1996-1999, interviews 1999 and clustering 2000-2001. Even though the results of different cases were somewhat conflicting here are the central results from the analysis of Iltalehti Online 1995-2001: - Reading was strongly determined by the gender. - The structure of Iltalehti Online guided the reading strongly. - People did not make a clear distinction in content between news and entertainment. - Users created new habits in their everyday life during the first years of using Iltalehti Online. These habits were categorized as follows: - break between everyday routines - established habit - new practice within the rhythm of the day - In the clustering of the users sports, culture and celebrities were the most distinguishing contents. Users did not move across these borders as much as within them. The dissertation gives contribution to the development of multidimensional research methods in the field of emerging phenomena in media field. It is also a unique description of a phase of development in media history through an unique research material. There is no such information (logs + demographics) available of any other Finnish online news media. Either from the first years or today.

Machine recognition of online handwritten Devanagari characters

In this paper, we describe a system for the automatic recognition of isolated handwritten Devanagari characters obtained by linearizing consonant conjuncts. Owing to the large number of characters and resulting demands on data acquisition, we use structural recognition techniques to reduce some characters to others. The residual characters are then classified using the subspace method. Finally the results of structural recognition and feature-based matching are mapped to give final output. The proposed system Ifs evaluated for the writer dependent scenario.

An automated framework for understanding structural variations in the binding grooves of MHC class II molecules

Background: MHC/HLA class II molecules are important components of the immune system and play a critical role in processes such as phagocytosis. Understanding peptide recognition properties of the hundreds of MHC class II alleles is essential to appreciate determinants of antigenicity and ultimately to predict epitopes. While there are several methods for epitope prediction, each differing in their success rates, there are no reports so far in the literature to systematically characterize the binding sites at the structural level and infer recognition profiles from them. Results: Here we report a new approach to compare the binding sites of MHC class II molecules using their three dimensional structures. We use a specifically tuned version of our recent algorithm, PocketMatch. We show that our methodology is useful for classification of MHC class II molecules based on similarities or differences among their binding sites. A new module has been used to define binding sites in MHC molecules. Comparison of binding sites of 103 MHC molecules, both at the whole groove and individual sub-pocket levels has been carried out, and their clustering patterns analyzed. While clusters largely agree with serotypic classification, deviations from it and several new insights are obtained from our study. We also present how differences in sub-pockets of molecules associated with a pair of autoimmune diseases, narcolepsy and rheumatoid arthritis, were captured by PocketMatch(13). Conclusion: The systematic framework for understanding structuralvariations in MHC class II molecules enables large scale comparison of binding grooves and sub-pockets, which is likely to have direct implications towards predicting epitopes and understanding peptide binding preferences.

An automated framework for understanding structural variations in the binding grooves of MHC class II molecules

Background: MHC/HLA class II molecules are important components of the immune system and play a critical role in processes such as phagocytosis. Understanding peptide recognition properties of the hundreds of MHC class II alleles is essential to appreciate determinants of antigenicity and ultimately to predict epitopes. While there are several methods for epitope prediction, each differing in their success rates, there are no reports so far in the literature to systematically characterize the binding sites at the structural level and infer recognition profiles from them. Results: Here we report a new approach to compare the binding sites of MHC class II molecules using their three dimensional structures. We use a specifically tuned version of our recent algorithm, PocketMatch. We show that our methodology is useful for classification of MHC class II molecules based on similarities or differences among their binding sites. A new module has been used to define binding sites in MHC molecules. Comparison of binding sites of 103 MHC molecules, both at the whole groove and individual sub-pocket levels has been carried out, and their clustering patterns analyzed. While clusters largely agree with serotypic classification, deviations from it and several new insights are obtained from our study. We also present how differences in sub-pockets of molecules associated with a pair of autoimmune diseases, narcolepsy and rheumatoid arthritis, were captured by PocketMatch(13). Conclusion: The systematic framework for understanding structural variations in MHC class II molecules enables large scale comparison of binding grooves and sub-pockets, which is likely to have direct implications towards predicting epitopes and understanding peptide binding preferences.

Three-year-olds are sensitive to semantic prominence during online language comprehension

Recent evidence from adult pronoun comprehension suggests that semantic factors such as verb transitivity affect referent salience and thereby anap- hora resolution. We tested whether the same semantic factors influence pronoun comprehension in young children. In a visual world study, 3-year- olds heard stories that began with a sentence containing either a high or a low transitivity verb. Looking behaviour to pictures depicting the subject and object of this sentence was recorded as children listened to a subsequent sentence containing a pronoun. Children showed a stronger preference to look to the subject as opposed to the object antecedent in the low transitivity condition. In addition there were general preferences (1) to look to the subject in both conditions and (2) to look more at both potential antecedents in the high transitivity condition. This suggests that children, like adults, are affected by semantic factors, specifically semantic prominence, when interpreting anaphoric pronouns.

Advanced transmission electron microscope triboprobe with automated closed-loop nanopositioning

Here the design and operation of a novel transmission electron microscope (TEM) triboprobe instrument with real-time vision control for advanced in situ electron microscopy is demonstrated. The NanoLAB triboprobe incorporates a new high stiffness coarse slider design for increased stability and positioning performance. This is linked with an advanced software control system which introduces both new and flexible in situ experimental functional testing modes, plus an automated vision control feedback system. This advancement in instrumentation design unlocks new possibilities of performing a range of new dynamical nanoscale materials tests, including novel friction and fatigue experiments inside the electron microscope.

Resolving Ambiguities in Confused Online Tamil Characters with Post Processing Algorithms

This paper addresses the problem of resolving ambiguities in frequently confused online Tamil character pairs by employing script specific algorithms as a post classification step. Robust structural cues and temporal information of the preprocessed character are extensively utilized in the design of these algorithms. The methods are quite robust in automatically extracting the discriminative sub-strokes of confused characters for further analysis. Experimental validation on the IWFHR Database indicates error rates of less than 3 % for the confused characters. Thus, these post processing steps have a good potential to improve the performance of online Tamil handwritten character recognition.

Boost Customer Loyalty with Online Support. The Case of Mobile Telecoms Providers.

The paper explores the effect of customer satisfaction with online supporting services on loyalty to providers of an offline core service. Supporting services are provided to customers before, during, or after the purchase of a tangible or intangible core product, and have the purpose of enhancing or facilitating the use of this product. The internet has the potential to dominate all other marketing channels when it comes to the interactive and personalised communication that is considered quintessential for supporting services. Our study shows that the quality of online supporting services powerfully affects satisfaction with the provider and customer loyalty through its effect on online value and enjoyment. Managerial implications are provided.

Comparative study of filter-bank mean-energy distance for automated segmentation of speech signals

This paper describes a method of automated segmentation of speech assuming the signal is continuously time varying rather than the traditional short time stationary model. It has been shown that this representation gives comparable if not marginally better results than the other techniques for automated segmentation. A formulation of the 'Bach' (music semitonal) frequency scale filter-bank is proposed. A comparative study has been made of the performances using Mel, Bark and Bach scale filter banks considering this model. The preliminary results show up to 80 % matches within 20 ms of the manually segmented data, without any information of the content of the text and without any language dependence. 'Bach' filters are seen to marginally outperform the other filters.

Language independent automated segmentation of speech using Bach scale filter-banks

This correspondence describes a method for automated segmentation of speech. The method proposed in this paper uses a specially designed filter-bank called Bach filter-bank which makes use of 'music' related perception criteria. The speech signal is treated as continuously time varying signal as against a short time stationary model. A comparative study has been made of the performances using Mel, Bark and Bach scale filter banks. The preliminary results show up to 80 % matches within 20 ms of the manually segmented data, without any information of the content of the text and without any language dependence. The Bach filters are seen to marginally outperform the other filters.

Online association policies in IEEE 802.11 WLANs

In this paper, we study the performance of client-Access Point (AP) association policies in IEEE 802.11 based WLANs. In many scenarios, clients have a choice of APs with whom they can associate. We are interested in finding association policies which lead to optimal system performance. More specifically, we study the stability of different association policies as a function of the spatial distribution of arriving clients. We find for each policy the range of client arrival rates for which the system is stable. For small networks, we use Lyapunov function methods to formally establish the stability or instability of certain policies in specific scenarios. The RAT heuristic policy introduced in our prior work is shown to have very good stability properties when compared to several other natural policies. We also validate our analytical results by detailed simulation employing the IEEE 802.11 MAC.

Online client-AP association in WLANs

In many IEEE 802.11 WLAN deployments, wireless clients have a choice of access points (AP) to connect to. In current systems, clients associate with the access point with the strongest signal to noise ratio. However, such an association mechanism can lead to unequal load sharing, resulting in diminished system performance. In this paper, we first provide a numerical approach based on stochastic dynamic programming to find the optimal client-AP association algorithm for a small topology consisting of two access points. Using the value iteration algorithm, we determine the optimal association rule for the two-AP topology. Next, utilizing the insights obtained from the optimal association ride for the two-AP case, we propose a near-optimal heuristic that we call RAT. We test the efficacy of RAT by considering more realistic arrival patterns and a larger topology. Our results show that RAT performs very well in these scenarios as well. Moreover, RAT lends itself to a fairly simple implementation.