waashk/dblp huggingface dataset:
ccvisu: automatic visual software decomposition. understanding the structure of large existing (and evolving) software systems is a major challenge for software engineers. in reverse engineering, we aim to compute, for a given software system, a decomposition of the system into its subsystems. ccvisu is a lightweight tool that takes as input a software graph model and computes a visual representation of the system's structure, i.e., it structures the system into separated groups of artifacts that are strongly related, and places them in a 2- or 3-dimensional space. besides the decomposition into subsystems, it reveals the relatedness between the subsystems via interpretable distances. the tool reads a software graph from a simple text file in rsf format, e.g., call, inheritance, containment, or co-change graphs. the resulting system structure is currently either directly presented on the screen, or written to an output file in svg, vrml, or plain text format. the tool is designed as a reusable software component, easy to use, and easy to integrate into other tools; it is based on efficient algorithms and supports several formats for data interchange.
viedame - flexible and robust bpel processes through monitoring and adaptation. viedame is a tool for monitoring and dynamic service adaptation of bpel processes. the tool monitors partner service interaction to compute quality of service (qos) data and performs dynamic service adaptation based on various available service selection strategies, such as availability or response time. the service adaptation happens transparently at runtime by dynamically selecting an alternative service for an existing service used in the process, without any changes to the deployed bpel process. to gain even more flexibility, the tool provides transformation components to compensate service interface mismatches, which are likely to occur when using alternative services offered by different providers.
performance modeling for service oriented architectures. we present a tool for performance modeling of service oriented architectures (soas). as mission-critical use of whole-of-government soas become pervasive, the capability to model and predict the run-time performance of interdependent composite applications is critical. the tool can be used by architects early in the software engineering lifecycle to predict performance and scalability, to evaluate architectural alternatives, to provide guidance for capacity planning and the negotiation of service level agreements (slas). it directly models and produces metrics for soa applications in terms that are familiar to architects (services, workflows, and compositions of services). the tool enables the performance model to be generated from available architectural artifacts and performance data, making it easy to use. it is highly dynamic to facilitate interactive evaluation of alternative architectural choices. the tool can model complex deployment scenarios such as server virtualisation. development and evaluation of the tool was carried out in the context of architectural modeling for large-scale soa-based australian e-government systems. the tool radically simplified the construction and execution of soa performance models, and contributed critical insights for the architecting of these systems.
second international workshop on ultra-large-scale software-intensive systems (ulssis 2008). software-intensive systems now being envisioned and in some cases developed will surpass complexity thresholds beyond which established software engineering concepts, methods and tools no longer work well. ulssis provides a forum in which researchers from industry, academia and governments come together to understand these issues and to discuss research and development activities to address them.
safety of component-based systems: analysis and improvement using fujaba4eclipse. todays embedded and safety-critical systems incorporate increasing amounts of software. consequently, the software architecture and its connection to hardware elements have a big impact on the safety of those systems. we present in this paper an approach and its implementation in the fujaba4eclipse environment for the analysis and improvement of component-based systems w.r.t. their safety which specifically exploits the software and system structure.
modeling in software engineering. the modeling in software engineering (mise) workshops are a collaboration between the icse and models research communities, with a focus on using models to facilitate software development.
an integrated aspect-oriented model-driven software product line tool suite. software product line engineering is mostly about the systematic management of commonality and variability between product line members. the effectiveness of this approach thus very much depends on how well variability within the family of similar products is implemented and managed. variability often has widespread impact, crosscutting not only multiple parts of individual artifacts but also multiple artifacts in multiple stages of the product line lifecycle. this demonstration presents an approach that facilitates variability implementation, management, and tracing by integrating aspect-oriented and model-driven software development. we demonstrate means for effectively dealing with variability on model, model transformation, and code generation level. the concepts are illustrated with a case study of a home automation system.
an analyzer for extended compositional process algebras. system simulation and verification become more demanding as complexity grows. pat is developed as an interactive system to support composing, simulating and reasoning of process algebra with various extensions like fairness events, global variables and parameterized processes. pat provides user friendly interfaces to support system modeling and simulation. furthermore, it embeds two complementing model checking techniques catering for different systems and properties, namely, an explicit on-the-fly model checker which is designed to verify event-based fairness constraints efficiently and a bounded model checker based on state-of-the-art sat solvers. the model checkers are capable of proving reachability, deadlock-freeness and the full set of linear temporal logic (ltl) properties. compared to other model checkers, pat has two key advantages. firstly, it supports an intuitive annotation of fairness constraints so that it handles large number of fairness constraints efficiently. secondly, the compositional encoding of system models as sat problems allows us to handle compositional process algebra effectively. the experimental results show that pat is capable of verifying systems with large number of states and outperforms the state-of-the-art model checkers in some cases.
a verification system for timed interval calculus. timed interval calculus (tic) is a highly expressive set-based notation for specifying and reasoning about embedded real-time systems. however, it lacks mechanical proving support, as its verification usually involves infinite time intervals and continuous dynamics. in this paper, we develop a system based on a generic theorem prover, prototype verification system (pvs), to assist formal verification of tic at a high grade of automation. tic semantics has been constructed by the pvs typed higher-order logic. based on the encoding, we have checked all tic reasoning rules and discovered subtle flaws. a translator has been implemented in java to automatically transform tic models into pvs specifications. a collection of supplementary rules and pvs strategies has been defined to facilitate the rigorous reasoning of tic models with functional and non-functional (for example, real-time) requirements at the interval level. our approach is generic and can be applied further to support other real-time notations.
prototypes for medical case-based applications. already in the early stages of case-based reasoning prototypes were considered as an interesting technique to structure the case base and to fill the knowledge gap between single cases and general knowledge. unfortunately, later on prototypes never became a hot topic within the cbr community. however, for medical applications they have been used rather regularly, because they correspond to the reasoning of doctors in a natural way. in this paper, we illustrate the role of prototypes by application programs, which cover all typical medical tasks: diagnosis, therapy, and course analysis.
an infrastructure for mining medical multimedia data. biomedical research processes related to disease diagnosis, prognosis and monitoring would great benefit from advanced tools able not exclusively to store and manage multimodal data but also to process and extract significant relations and then novel knowledge from them. indeed, making a prediction on a disease outcome usually requires considering heterogeneous pieces of information obtained from several sources which should be compared and related. mining medical multimedia objects is aimed at discovering and making available the hidden useful knowledge embedded in collections of data and is, then, of key importance for supporting clinical decision-making. in this paper, we report current results of a medical warehouse we are developing in an integrated environment for mining clinical data acquired by different media. in particular, focus is herein given to the infrastructure of the warehouse and its current functionalities not limited to storage and management but including intelligent representation and annotation of multimedia objects.
efficient string mining under constraints via the deferred frequency index. we propose a general approach for frequency based string mining, which has many applications, e.g. in contrast data mining. our contribution is a novel algorithm based on a deferred data structure. despite its simplicity, our approach is up to 4 times faster and uses about half the memory compared to the best-known algorithm of fischer et al. applications in various string domains, e.g. natural language, dna or protein sequences, demonstrate the improvement of our algorithm.
maximum margin active learning for sequence labeling with different length. sequence labeling problem is commonly encountered in many natural language and query processing tasks. svm struct is a supervised learning algorithm that provides a flexible and effective way to solve this problem. however, a large amount of training examples is often required to train svm struct , which can be costly for many applications that generate long and complex sequence data. this paper proposes an active learning technique to select the most informative subset of unlabeled sequences for annotation by choosing sequences that have largest uncertainty in their prediction. a unique aspect of active learning for sequence labeling is that it should take into consideration the effort spent on labeling sequences, which depends on the sequence length. a new active learning technique is proposed to use dynamic programming to identify the best subset of sequences to be annotated, taking into account both the uncertainty and labeling effort. experiment results show that our svm struct active learning technique can significantly reduce the number of sequences to be labeled while outperforming other existing techniques.
using data mining to build integrated discrete event simulations. building a system from disparate software requires analysis to establish commonality of code. the ability of a data mining tool to extract repeating functional structures is the first step to reduce exploration, save development time, and re-use software components. this case study looks specifically at the application of graph-based data mining algorithms to code re-factoring. after writing a module to obtain a graph representation of a discrete event model, we built a tool around the university of washington's subdue package to find recurring patterns of logic. this resulted in cleaner code and increased awareness of code re-use.
local modelling in classification. in classification tasks it may sometimes not be meaningful to build single rules on the whole data. this may especially be the case if the classes are composed of several subclasses. several common as well as recent issues are presented to solve this problem. as it can e.g. be seen in weihs et al. (2006) there may result strong benefit from such local modelling. all presented methods are evaluated and compared on four real-world classification problems in order to obtain some overall ranking of their performance following an idea of hornik and meyer (2007).
relative linkage disequilibrium: a new measure for association rules. association rules are one of the most popular unsupervised data mining methods. once obtained, the list of association rules extractable from a given dataset is compared in order to evaluate their importance level. the measures commonly used to assess the strength of an association rule are the indexes of support, confidence, and the lift.relative linkage disequilibrium (rld) was originally proposed as an approach to analyse both quantitatively and graphically general two way contingency tables. rld can be considered an adaptation of the lift measure with the advantage that it presents more effectively the deviation of the support of the whole rule from the support expected under independence given the supports of the lhs (a) and the rhs (b). rld can be interpreted graphically using a simplex representation leading to powerful graphical display of association relationships. moreover the statistical properties of rld are known so that confirmatory statistical tests of significance or basic confidence intervals can be applied.this paper will present the properties of rld in the context of association rules and provide several application examples to demonstrate it's practical advantages.
improving imbalanced multidimensional dataset learner performance with artificial data generation: density-based class-boost algorithm. improving the learner performance over imbalanced and multidimensional datasets raises a challenging task for machine learning community. although a salient characteristic in data modeling is the amount of data provided for the learner, the proportional distribution of that data in each class has also direct relationship with the classifier performance. in imbalanced datasets when data is distributed into different classes, various in size, understanding of data structure and characteristics plays an important role in improving the learner accuracy. in this paper we introduce a new approach that combines the information gained from traditional classification algorithms, confusion matrix parameters and density-based clustering to generate artificial data in order to increase the learner performance. first a classification algorithm is run on training data. then the confusion matrix is studied and the true positive (tp) rate of each class is measured. the class with the lowest tp rate is selected. using density-based clustering we identify the centroid of the class and measure the samples distribution in multidimensional space in the next step. with the values gained from probability density function estimations for clusters, extra samples are generated and added to the original dataset to rebalance the class proportion and the weight of different classes in the whole training set. our method has been evaluated in terms of tp, f-measure and also overall accuracy against a number of demetra (toxicology) and uci datasets. our method provides an insight view of the data structure and characteristics in order to identify how much and where the data need to be added for increasing the classification accuracy of the learner.
an efficient similarity searching algorithm based on clustering for time series. indexing large time series databases is crucial for efficient searching of time series queries. in the paper, we propose a novel indexing scheme rqi (range query based on index) which includes three filtering methods: first-k filtering, indexing lower bounding and upper bounding as well as triangle inequality pruning. the basic idea is calculating wavelet coefficient whose first k coefficients are used to form a mbr (minimal bounding rectangle) based on haar wavelet transform for each time series and then using point filtering method; at the same time, lower bounding and upper bounding feature of each time series is calculated, in advance, and stored into index structure. at last, triangle inequality pruning method is used by calculating the distance between time series beforehand. then we introduce a novel lower bounding distance function slbs (symmetrical lower bounding based on segment) and a novel clustering algorithm csa (clustering based on segment approximation) in order to further improve the search efficiency of point filtering method by keeping a good clustering trait of index structure. extensive experiments over both synthetic and real datasets show that our technologies provide perfect pruning power and could obtain an order of magnitude performance improvement for time series queries over traditional naive evaluation techniques.
webangels filter: a violent web filtering engine using textual and structural content-based analysis. the development of the web has been paralleled by the proliferation of harmful web pages content. using violent web page as a case study, we review some existing solutions, then we propose a violent web content detection and filtering system called "webangels filter" which uses textual and structural analysis. "webangels filter" has the advantage of combining several data mining algorithms for web site classification. we present a comparative study of different data mining techniques to block violent contentweb pages. also, we discuss how the combination learning based methods can improve filtering performances. our results show that it can detect and filter violent content effectively.
generalized graph matching for data mining and information retrieval. graph based data representation offers a convenient possibility to represent entities, their attributes, and their relationships to other entities. consequently, the use of graph based representation for data mining has become a promising approach to extracting novel and useful knowledge from relational data. in order to check whether a certain graph occurs, as a substructure, within a larger database graph, the widely studied concept of subgraph isomorphism can be used. however, this conventional approach is rather limited. in the present paper the concept of subgraph isomorphism is substantially extended such that it can cope with don't care symbols, variables, and constraints. our novel approach leads to a powerful graph matching methodology which can be used for advanced graph based data mining.
data mining with neural networks for wheat yield prediction. precision agriculture (pa) and information technology (it) are closely interwoven. the former usually refers to the application of nowadays' technology to agriculture. due to the use of sensors and gps technology, in today's agriculture many data are collected. making use of those data via it often leads to dramatic improvements in efficiency. for this purpose, the challenge is to change these raw data into useful information. in this paper we deal with neural networks and their usage in mining these data. our particular focus is whether neural networks can be used for predicting wheat yield from cheaply-available in-season data. once this prediction is possible, the industrial application is quite straightforward: use data mining with neural networks for, e.g., optimizing fertilizer usage, in economic or environmental terms.
weighted association rule mining from binary and fuzzy data. a novel approach is presented for mining weighted association rules (ars) from binary and fuzzy data. we address the issue of invalidation of downward closure property (dcp) in weighted association rule mining where each item is assigned a weight according to its significance w.r.t some user defined criteria. most works on weighted association rule mining so far struggle with invalid downward closure property and some assumptions are made to validate the property. we generalize the weighted association rule mining problem for databases with binary and quantitative attributes with weighted settings. our methodology follows an apriori approach [9] and avoids pre and post processing as opposed to most weighted association rule mining algorithms, thus eliminating the extra steps during rules generation. the paper concludes with experimental results and discussion on evaluating the proposed approach.
an exploration into the power of formal concept analysis for domestic violence analysis. the types of police inquiries performed are very diverse in nature and the current data processing architecture is not sufficiently tailored to cope with this diversity. many information concerning cases is still stored in databases as unstructured text. formal concept analysis is showcased as an exploratory data analysis technique for discovering new knowledge from police reports. it turns out that it provides a powerful framework for exploring the dataset, resulting in essential knowledge for improving current practices. it is shown that the domestic violence definition employed by the police organisation of the netherlands is not always as clear as it should be, making it hard to use it effectively for classification purposes. in addition, newly discovered knowledge for automatically classifying certain cases as either domestic or non-domestic violence is presented. moreover, essential techniques for detecting incorrect classifications, performed by police officers, are provided. finally, some problems encountered because of the sometimes unstructured way of working of police officers are discussed. both using formal concept analysis for exploratory data analysis and its application on this area are novel enough to make this paper into a valuable contribution to the literature.
contrast-set mining of aircraft accidents and incidents. identifying patterns of factors associated with aircraft accidents is of high interest to the aviation safety community. however, accident data is not large enough to allow a significant discovery of repeating patterns of the factors. we applied the stucco algorithm to analyze aircraft accident data in contrast to the aircraft incident data in major aviation safety databases and identified factors that are significantly associated with the accidents. the data pertains to accidents and incidents involving commercial flights within the united states. the ntsb accident database was analyzed against four incident databases and the results were compared. we ranked the findings by the factor support ratio, a measure introduced in this work.
an application for electroencephalogram mining for epileptic seizure prediction. a computational framework to support seizure predictions in epileptic patients is presented. it is based on mining and knowledge discovery in electroencephalogram (eeg) signal. a set of features is extracted and classification techniques are then used to eventually derive an alarm signal predicting a coming seizure. the epileptic patient may then take steps in order to prevent accidents and social exposure.