Enterprise systems are inherently complex and maintaining their full, up-to-date overview poses a serious challenge to the enterprise architects' teams.This problem encourages the search for automated means of discovering knowledge about such systems.An important aspect of this knowledge is understanding the data that are processed by applications and their relationships.In our previous work, we used application logs of an enterprise system to derive knowledge about the interactions taking place between applications.In this paper, we further explore logs to discover correspondence between data processed by different applications.Our contribution is the following: we propose a method for discovering relationships between data using log analysis, we validate our method against a benchmark system AcmeAir and we validate our method against a real-life system running at Nordea Bank.

Abstract4Application logs record the behavior of a system during its runtime and their analysis can provide useful information.In this article, we propose a method of automated log analysis to discover interactions taking place between applications in an enterprise.We believe that such an automated approach can greatly support enterprise architects in building an up-to-date view of a governed system in a modern, fast-paced development environment.Our contribution is the following: we propose a new method for log template generation called SLT (Simple Log Template), we propose a method of extracting knowledge about application interactions from logs, and we validate the proposed methods on a real system running at Nordea Bank.Additionally, we collect statistical information about application logs from the real-life system, based on which we formulate some observations that support our method. Discovering interactions between applications with log analysis

Recommender systems are software tools and techniques which aim at suggesting to users items they might be interested in.Context-aware recommender systems are a particular category of recommender systems which exploit contextual information to provide more adequate recommendations.However, recommendation engines still suffer from the cold-start problem, namely where not enough information about users and their ratings is available.In this paper we introduce a method for generating a list of top k recommendations in a new user cold-start situations.It is based on a user model called Contextual Conditional Preferences and utilizes a satisfiability measure proposed in this paper.We analyze accuracy measures as well as serendipity, novelty and diversity of results obtained using three context-aware publicly available datasets in comparison with several contextual and traditional state-of-the-art baselines.We show that our method is applicable in the new user cold-start situations as well as in typical scenarios.

Context-aware Recommender Systems aim to provide users with the most adequate recommendations for their current situation.However, an exact context obtained from a user could be too specific and may not have enough data for accurate rating prediction.This is known as the data sparsity problem.Moreover, often user preference representation depends on the domain or the specific recommendation approach used.Therefore, a big effort is required to change the method used.In this paper we present a new approach for contextual pre-filtering (i.e. using the current context to select a relevant subset of data).Our approach can be used with existing recommendation algorithms.It is based on two ontologies: Recommender System Context ontology, which represents the context, and Contextual Ontological User Profile ontology, which represents user preferences.We evaluated our approach through an offline study which showed that when used with well-known recommendation algorithms it can significantly improve the accuracy of prediction.

article Free Access Share on Software quality lab in the informatics option of Gdañsk TU electronics faculty Authors: Krzysztof Goczyla Technical University of Gdańsk, Poland Technical University of Gdańsk, PolandView Profile , Stanislaw Szejko Technical University of Gdańsk, Poland Technical University of Gdańsk, PolandView Profile Authors Info & Claims ACM SIGCSE BulletinVolume 28Issue SI1996 pp 133–135https://doi.org/10.1145/237477.237571Published:01 January 1996Publication History 0citation164DownloadsMetricsTotal Citations0Total Downloads164Last 12 Months15Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF