Using hypergraph clustering for software architecture reconstruction of data-tier software

Software architecture reconstruction techniques aim at recovering software architecture documentation regarding a software system. These techniques mainly analyze coupling/dependencies among the software modules to group them and reason about the high-level structure of the system. Hereby, inter-dependencies among the software modules are mainly represented with design structure matrices or regular directed/undirected graphs. In this paper, we introduce a software architecture reconstruction approach that utilizes hypergraphs for representing inter-module dependencies. We show that these models are more appropriate for capturing dependencies other than direct call relations. We illustrate the application of the approach with an industrial PL/SQL program from the telecommunications domain. PL/SQL programs are mainly composed of procedures that are coupled due to commonly accessed database elements. We analyze and represent these dependencies in the form of a hypergraph. Then, we perform modularity clustering on this model and propose a packaging structure to the designer accordingly. We observed promising results in comparison with previous work. The accuracy of the results were also approved by domain experts. Turkey http://www.turkcell.com.tr Kaya [email protected] Turkey Sabanci University ://people.sabanciuniv.edu/kaya/ [email protected] Turkey Turkcell http://www.turkcell.com.tr Hasan Sozer [email protected] Turkey Ozyegin University http://faculty.ozyegin.edu.tr/hsozer

Self-Supervised Learning for Recommender Systems: A Survey

In recent years, neural architecture-based recommender systems have achieved tremendous success, but they still fall short of expectation when dealing with highly sparse data. Self-supervised learning (SSL), as an emerging technique for learning from unlabeled data, has attracted considerable attention as a potential solution to this issue. This survey paper presents a systematic and timely review of research efforts on self-supervised recommendation (SSR). Specifically, we propose an exclusive definition of SSR, on top of which we develop a comprehensive taxonomy to divide existing SSR methods into four categories: contrastive, generative, predictive, and hybrid. For each category, we elucidate its concept and formulation, the involved methods, as well as its pros and cons. Furthermore, to facilitate empirical comparison, we release an open-source library SELFRec (https://github.com/Coder-Yu/SELFRec), which incorporates a wide range of SSR models and benchmark datasets. Through rigorous experiments using this library, we derive and report some significant findings regarding the selection of self-supervised signals for enhancing recommendation. Finally, we shed light on the limitations in the current research and outline the future research directions.Comment: 20 pages. Accepted by TKD

Database dependency analysis for PL/SQL programs

PL/SQL dili ile yazılan programlar, veri tabanı üzerinde prosedür ve fonksiyon objeleri, ve bu objelerin bir kümesini barındıran paket objeleri olarak geliştirilmektedirler. Bu objeler yoğun olarak tablo ve görünüm gibi veri tabanı objelerini kullanmaktadırlar. Mevcut analiz araçları ile her bir objenin hangi diğer objelere bağımlılığı olduğunu görmek mümkündür. Ancak bu bilgi paket seviyesinde sağlanıp, paketler içindeki her bir prosedür ve fonksiyonun hangi veri tabanı elemanlarını kullanıldığı bilgisine ulaşılamamaktadır. Özellikle uzun yıllardır idame edilen programlarda, paketler çok fazla sayıda prosedür ve fonksiyon barındırmaktadır ve bu paketlerin belirli zamanlarda parçalanması idame edilebilirlik açısından fayda sağlamaktadır. Bu amaçla programların yeniden yapılandırılması, değişikliklere ilişkin etki analizlerinin yapılabilmesine destek sağlayacak bir analiz aracı geliştirilmiştir. Bu araç, paketler içerisinde yer alan prosedür ve fonksiyonların kullandıkları ortak veri tabanı tablolarını tespit edebilmekte ve böylece değişiklik etki analizi ile tasarım kararlarına destek olmaktadır. Geliştirdiğimiz analiz aracı, bir teknoloji şirketindeki müşteri ilişkileri yönetimi sistemine uygulanmıştır.PL/SQL programs are composed of procedure and function objects deployed on a database. These objects can be grouped into a set of package objects and they extensively use database objects such as tables and views. Existing analysis tools can detect which objects are dependent on which other objects. However, this information is available only at the package level. It is not possible to detect database dependencies of procedures and functions that are encapsulated in packages. Existing packages might include many procedures and functions and they might have to be refactored to improve software maintainability, especially in the case of legacy systems that are maintained for years. In this work, we developed a dependency analysis tool to support software refactoring and impact analysis. This tool detects database dependencies of procedures and functions taking place in packages. It supports change impact analysis and design decisions by detecting database tables commonly accessed by various objects. We applied our tool on a customer relations management system maintained by a technology firm.Publisher versio

Helmholtz Portfolio Theme Large-Scale Data Management and Analysis (LSDMA)

The Helmholtz Association funded the "Large-Scale Data Management and Analysis" portfolio theme from 2012-2016. Four Helmholtz centres, six universities and another research institution in Germany joined to enable data-intensive science by optimising data life cycles in selected scientific communities. In our Data Life cycle Labs, data experts performed joint R&D together with scientific communities. The Data Services Integration Team focused on generic solutions applied by several communities

Recommending on graphs: a comprehensive review from a data perspective

Recent advances in graph-based learning approaches have demonstrated their effectiveness in modelling users' preferences and items' characteristics for Recommender Systems (RSS). Most of the data in RSS can be organized into graphs where various objects (e.g., users, items, and attributes) are explicitly or implicitly connected and influence each other via various relations. Such a graph-based organization brings benefits to exploiting potential properties in graph learning (e.g., random walk and network embedding) techniques to enrich the representations of the user and item nodes, which is an essential factor for successful recommendations. In this paper, we provide a comprehensive survey of Graph Learning-based Recommender Systems (GLRSs). Specifically, we start from a data-driven perspective to systematically categorize various graphs in GLRSs and analyze their characteristics. Then, we discuss the state-of-the-art frameworks with a focus on the graph learning module and how they address practical recommendation challenges such as scalability, fairness, diversity, explainability and so on. Finally, we share some potential research directions in this rapidly growing area.Comment: Accepted by UMUA

A review on green caching strategies for next generation communication networks

© 2020 IEEE. In recent years, the ever-increasing demand for networking resources and energy, fueled by the unprecedented upsurge in Internet traffic, has been a cause for concern for many service providers. Content caching, which serves user requests locally, is deemed to be an enabling technology in addressing the challenges offered by the phenomenal growth in Internet traffic. Conventionally, content caching is considered as a viable solution to alleviate the backhaul pressure. However, recently, many studies have reported energy cost reductions contributed by content caching in cache-equipped networks. The hypothesis is that caching shortens content delivery distance and eventually achieves significant reduction in transmission energy consumption. This has motivated us to conduct this study and in this article, a comprehensive survey of the state-of-the-art green caching techniques is provided. This review paper extensively discusses contributions of the existing studies on green caching. In addition, the study explores different cache-equipped network types, solution methods, and application scenarios. We categorically present that the optimal selection of the caching nodes, smart resource management, popular content selection, and renewable energy integration can substantially improve energy efficiency of the cache-equipped systems. In addition, based on the comprehensive analysis, we also highlight some potential research ideas relevant to green content caching

Machine Learning

Machine Learning can be defined in various ways related to a scientific domain concerned with the design and development of theoretical and implementation tools that allow building systems with some Human Like intelligent behavior. Machine learning addresses more specifically the ability to improve automatically through experience