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Facing the increasing global competition, modern business organizations have to respond quickly and correctly to the constant changing environment to ensure their competitive advantages. This goal has led to a recent surge of work on Business Process Reengineering (BPR) and Workflow Management. While most work in these areas assume that process definitions are known in a priori, it is widely recognized that defining a process type which totally represents all properties of the underlying business process is a difficult job. This job is currently practiced in a very ad-hoc fashion. In this paper, we postulate an algorithm to discover the process definition from analyzing the existing process instances. We compare our algorithm with other existing algorithms proposed in the literature in terms of time complexity and apply these algorithms through synthetic data sets to measure the qualities of output results. It has been found that our algorithm is able to return the process definitions closer to the real ones in a faster manner.

With the computerization of businesses, more and more data are generated and stored in databases for many business applications. Finding interesting patterns among those data may lead to useful knowledge that provides competitive advantage in business. Knowledge discovery in database has thus become an important issue to help business acquire knowledge that assists managerial and operational work. Among many types of knowledge, classification knowledge is widely used. Most classification rules learned by induction algorithms are in the crisp form. Fuzzy linguistic representation of rules, however, is much closer to the way human reasons. The objective of this research is to propose a method to mine classification knowledge from the database with fuzzy descriptions. The procedure contains five steps, starting from data preparation to rule pruning. A rule induction algorithm, RITIO, is employed to generate the classification rules. Fuzzy inference mechanism that includes fuzzy matching and output reasoning is specified to yield the output class. An experiment is conducted using several databases to show advantages of this work. The proposed method is justified with good system performance. It can be easily implemented in various business applications on classification tasks.

It is common to test many hypotheses simultaneously in the application of statistics. The probability of making a false discovery grows with the number of statistical tests performed. When all the null hypotheses are true, and the test statistics are indepen- dent and continuous, the error rates from the family wise error rate (FWER)- and the false discovery rate (FDR)-controlling procedures are equal to the nominal level. When some of the null hypotheses are not true, both procedures are conservative. In the first part of this study, we review the background of the problem and propose methods to estimate the number of true null hypotheses. The estimates can be used in FWER- and FDR-controlling procedures with a consequent increase in power. We conduct simulation studies and apply the estimation methods to data sets with bio- logical or clinical significance. In the second part of the study, we propose a mixture model approach for the analysis of ChIP-chip high density oligonucleotide array data to study the interac- tions between proteins and DNA. If we could identify the specific locations where proteins interact with DNA, we could increase our understanding of many important cellular events. Most experiments to date are performed in culture on cell lines, bac- teria, or yeast, and future experiments will include those in developing tissues, organs, or cancer biopsies, and they are critical in understanding the function of genes and proteins. Here we investigate the ChIP-chip data structure and use a beta-mixture model to help identify the binding sites. To determine the appropriate number of components in the mixture model, we suggest the Anderson-Darling testing. Our study indicates that it is a reasonable means of choosing the number of components in a beta-mixture model. The mixture model procedure has broad applications in biology and is illustrated with several data sets from bioinformatics experiments.

<p>This Master's Thesis describes the design and development of an architecture for automated network node discovery and topology analysis, implemented as an extension to the network management and provisioning system NETadmin. The architecture includes functionality for flexible network model assessment, using a method for versatile comparison between off-line database models and real-world models. These models are populated by current node data collected by network sensors.</p><p>The presented architecture supports (1) efficient creation and synchronization of network topology information (2) accurate recognition of new, replaced and upgraded nodes, including rogue nodes that may exhibit malicious behavior, and (3) provides an extension of an existing vendor-neutral enterprise network management and provisioning system.</p><p>An evaluation of the implementation shows evidence of accurate discovery and classification of unmatched hosts in a live customer production network with over 400 nodes, and presents data on performance and scalability levels.</p><p>The work was carried out at Netadmin System i Sverige AB, in Linköping, Sweden.</p>

Diss. (sammanfattning) Göteborg : Göteborgs universitet, 2010.

Abstract Fluorescent molecules used as detection probes and sensors provide vital information about the chemical events in living cells. Despite the large variety of available fluorescent dyes, new improved fluorogenic systems are of continued interest. The Diaryl-substituted Maleimides (DMs) exhibit excellent photophysical properties but have remained unexplored in bioscience applications. Herein we present the identification and full spectroscopic characterization of 3,4-bis(2,4-difluorophenyl)-maleimide and its first reported use as a donor component in Forster resonance energy transfer (FRET) systems. The FRET technique is often used to visualize proteins and to investigate protein-protein interactions in vitro as well as in vivo. The analysis of the photophysical properties of 3,4-bis(2,4-difluorophenyl)-maleimide revealed a large Stokes shift of 140 nm in MeOH, a very good fluorescence quantum yield in DCM (Ffl 0.61), and a high extinction coefficient ε(340) 48,400 M-1cm-1, thus ranking this molecule as superior over other reported moieties from this class. In addition, 3,4-bis(2,4-difluorophenyl)-maleimide was utilized as a donor component in two FRET systems wherein different molecules were chosen as suitable acceptor components - a fluorescent quencher (DABCYL) and another compatible fluorophore, tetraphenylporphyrin (TPP). It has been demonstrated that by designing a FRET peptide which contains the DM donor moiety and the acceptor (quencher) motif, a depopulation of the donor excited state occurred via intermolecular FRET mechanism, provided that the pairs were in close proximity. The Forster-Radius (R0) calculated for this FRET system was 36 % and a Forster-Radius (R0) of 26 % was determined for the second FRET system which contained TPP as an acceptor. The excellent photophysical properties of this fluorophore reveal a great potential for further bioscience applications. The 3,4-bis(2,4-difluorophenyl)-maleimide fluorescent moiety was also implemented in an alternative application targeting the enzyme carbonic anhydrase (CAs) are metalloenzymes that regulate essential physiologic and physio-pathological processes in different tissues and cells, and modulation of their activities is an efficient path to treating a wide range of human diseases. Developing more selective CA fluorescent probes as imaging tools is of significant importance for the diagnosis and treatment of cancer related disorders. The kinetic TGS approach is an efficient and reliable lead discovery strategy in which the biological target of interest is directly involved in the selection and assembly of the fragments together to generate its own inhibitors. Herein, we investigated whether the in situ click chemistry approach can be implemented in the design of novel CA inhibitors from a library of non-sulfonamide containing scaffolds, which has not been reported in the literature. In addition, we exploit the incorporation of the (recently reported by us) fluorescent moiety 3,4-bis(2,4-difluorophenyl)-maleimide) as a potential biomarker with affinity to CA, as well as two coumaine derivatives representing a newly discovered class of inhibitors. The screening of a set of library with eight structurally diverse azides AZ1-AZ8 and fifteen functionalized alkynes AK1-AK12 led to the identification of 8 hit combinations among which the most prominent ones were those containing the coumarine and fluorescent maleimide scaffolds. The syn- and anti-tirazole hit combinations, AK1AZ2, AK1AZ3, AK4AZ2, and AK4AZ3 were synthesized, and in a regioisomer-assignment co-injection test it was determined that the enzyme favored the formation of the anti-triazoles for all identified combinations. The mechanism of inhibition of these triazoles was validated by incubating the alkyne/azide scaffolds in the presence of Apo-CA (non-Zn containing) enzyme. It was demonstrated that the Zn-bound water/hydroxide was needed in order to hydrolyze the coumarins which generated the actual inhibitor, the corresponding hydroxycinnamic acid. The time dependent nature of the inhibition activity typical for all coumarine-based inhibitors was also observed for the triazole compounds whose inhibition constants (Ki) were determined in two independent experiments with pre-incubation times of 3 and 25 minutes, respectively. It was observed that the lower Ki values were determined, the longer the pre-incubations lasted. Thus, a novel type of coumarin-containing triazoles were presented as in situ generated hits which have the potential to be used as fluorescent bio-markers or other drug discovery applications. The proteins from the Bcl-2 family proteins play a central role in the regualtion of normal cellular homeostasis and have been validated as a target for the development of anticancer agents. Herein, in a proof-of-concept study based on a previous kinetic TGS study targeting Bcl-XL, it was demonstrated that a multi-fragment kinetic TGS approach coupled with TQMS technology was successfully implemented in the identification of known protein-protein modulators. Optimized screening conditions utilizing a triple quadruple mass spectrometer in the Multiple Reaction Monitoring (MRM) mode was demonstrated to be very efficient in kinetic TGS hit identification increasing both the throughput and sensitivity of this approach. The multi-fragment incubation approach was studied in detail and it was concluded that 200 fragment combinations in one well is an optimal and practical number permitting good acylsulfonamide detectability. Subsequently, a structurally diverse liberty of forty five thio acids and thirty eight sulfonyl azides was screened in parallel against Mcl-1 and Bcl-XL, and several potential hit combinations were identified. A control testing was carried out by substituting Bcl-XL with a mutant R139ABcl-XL, used to confirm that the potential kinetic TGS hit combinations were actually forming at the protein's hot spot and not elsewhere on the protein surface. Although, the synthesis of all these kinetic TGS hit compounds is currently ongoing, preliminary testing of several acylsulfonamides indicate that they disrupt the Bcl-XL/Bim or Mcl-1/Bim interaction.

The current trend in visual recognition research is to place a strict division between the supervised and unsupervised learning paradigms, which is problematic for two main reasons. On the one hand, supervised methods require training data for each and every category that the system learns; training data may not always be available and is expensive to obtain. On the other hand, unsupervised methods must determine the optimal visual cues and distance metrics that distinguish one category from another to group images into semantically meaningful categories; however, for unlabeled data, these are unknown a priori. I propose a visual category discovery framework that transcends the two paradigms and learns accurate models with few labeled exemplars. The main insight is to automatically focus on the prevalent objects in images and videos, and learn models from them for category grouping, segmentation, and summarization. To implement this idea, I first present a context-aware category discovery framework that discovers novel categories by leveraging context from previously learned categories. I devise a novel object-graph descriptor to model the interaction between a set of known categories and the unknown to-be-discovered categories, and group regions that have similar appearance and similar object-graphs. I then present a collective segmentation framework that simultaneously discovers the segmentations and groupings of objects by leveraging the shared patterns in the unlabeled image collection. It discovers an ensemble of representative instances for each unknown category, and builds top-down models from them to refine the segmentation of the remaining instances. Finally, building on these techniques, I show how to produce compact visual summaries for first-person egocentric videos that focus on the important people and objects. The system leverages novel egocentric and high-level saliency features to predict important regions in the video, and produces a concise visual summary that is driven by those regions. I compare against existing state-of-the-art methods for category discovery and segmentation on several challenging benchmark datasets. I demonstrate that we can discover visual concepts more accurately by focusing on the prevalent objects in images and videos, and show clear advantages of departing from the status quo division between the supervised and unsupervised learning paradigms. The main impact of my thesis is that it lays the groundwork for building large-scale visual discovery systems that can automatically discover visual concepts with minimal human supervision. / text

Although the Mexican population continues to be the largest Hispanic group in the United States, educational attainment is not increasing at a proportionate rate. First generation, Mexican-American students continue to have low enrollment in higher education institutions and high levels of attrition. Socioeconomic variables and ethnicity have correlated highly with these outcomes for thirty years, and programs have proliferated to address them, without much impact. Perhaps we need new approaches. This study investigates the lived experience of students attempting a university education. The goal of this research was to take the topic of educational achievement one step further by exploring identity development factors for first generation, Mexican-American college students via personal narratives. Researchers have long observed that people come to make sense of life via stories (Bruner, 1990; McAdams, 1985; Sarbin, 1986). Personal stories help to make sense of the past as well as foresee the future while helping to define current identity via recalling and/or retelling stories, particularly for emerging adults. Identity creation and negation was explored via McAdams’ life story model of identity: identity is an ever changing life story that strives for psychosocial unity and seeks purpose in relation to the world. The data collected from sixteen first generation, Mexican-American students at one university revealed that factors such as familial connections, cultural capital, generational immigration status, and self-discovery opportunities contribute to the ongoing creation and negotiation of identity. The outcome was the development of an identity soundboard which provides a visual representation of identity factors, each with its own control button, which is constantly adjusted according to individual experiences and narratives. The significance of these results is two-fold. One, it provides students and educators with a new perspective on identity development which can translate into new ways to address academic retention, attrition, and success. And two, it provides identity researchers with a new, customizable model with which to explore a variety of identity development processes, adaptable to specific research interests. The Hispanic community is a key player to the nation’s economic future, making efforts to foster a well-educated workforce a priority. Colleges and universities stand to benefit from a tailored approach to outreach and retention of students. It is by obtaining a glimpse of students’ reality that we can, as faculty, staff, and administrators, make changes that can positively affect their educational experience and outcome. / text

Accurate prediction of protein-ligand binding affinity is essential to computational drug discovery. Current approaches are limited by the accuracy of the underlying potential energy model that describes atomic interactions. A more rigorous physical model is critical for evaluating molecular interactions to chemical accuracy. The objective of this thesis research is to develop a polarizable force field with an accurate representation of electrostatic interactions, and apply this model to protein-ligand recognition and to ultimately solve practical problems in computer aided drug discovery. By calculating the hydration free energies of a series of organic small molecules, an optimal protocol is established to develop the electrostatic parameters from quantum mechanics calculations. Next, the systematical development and parameterization procedure of AMOEBA protein force field is presented. The derived force field has gone through extensive validations in both gas phase and condensed phase. The last part of the thesis involves the application of AMOEBA to study protein-ligand interactions. The binding free energies of benzamidine analogs to trypsin using molecular dynamics alchemical perturbation are calculated with encouraging accuracy. AMOEBA is also used to study the thermodynamic effect of constraining and hydrophobicity on binding energetics between phosphotyrosine(pY)-containing tripeptides and the SH2 domain of growth receptor binding protein 2 (Grb2). The underlying mechanism of an "entropic paradox" associated with ligand preorganization is explored. / text