7,069 research outputs found
Reverse Engineering Heterogeneous Applications
Nowadays a large majority of software systems are built using various technologies that in turn rely on different languages (e.g. Java, XML, SQL etc.). We call such systems heterogeneous applications (HAs). By contrast, we call software systems that are written in one language homogeneous applications. In HAs the information regarding the structure and the behaviour of the system is spread across various components and languages and the interactions between different application elements could be hidden. In this context applying existing reverse engineering and quality assurance techniques developed for homogeneous applications is not enough. These techniques have been created to measure quality or provide information about one aspect of the system and they cannot grasp the complexity of HAs. In this dissertation we present our approach to support the analysis and evolution of HAs based on: (1) a unified first-class description of HAs and, (2) a meta-model that reifies the concept of horizontal and vertical dependencies between application elements at different levels of abstraction. We implemented our approach in two tools, MooseEE and Carrack. The first is an extension of the Moose platform for software and data analysis and contains our unified meta-model for HAs. The latter is an engine to infer derived dependencies that can support the analysis of associations among the heterogeneous elements composing HA. We validate our approach and tools by case studies on industrial and open-source JEAs which demonstrate how we can handle the complexity of such applications and how we can solve problems deriving from their heterogeneous nature
The Ideal Candidate. Analysis of Professional Competences through Text Mining of Job Offers
The aim of this paper is to propose analytical tools for identifying peculiar aspects of job market for graduates. We propose a strategy for dealing with daa tat have different source and nature
A Survey on Web Usage Mining, Applications and Tools
World Wide Web is a vast collection of unstructured web documents like text, images, audio, video or Multimedia content. As web is growing rapidly with millions of documents, mining the data from the web is a difficult task. To mine various patterns from the web is known as Web mining. Web mining is further classified as content mining, structure mining and web usage mining. Web usage mining is the data mining technique to mine the knowledge of usage of web data from World Wide Web. Web usage mining extracts useful information from various web logs i.e. users usage history. This is useful for better understanding and serve the people for better web applications. Web usage mining not only useful for the people who access the documents from the World Wide Web, but also it useful for many applications like e-commerce to do personalized marketing, e-services, the government agencies to classify threats and fight against terrorism, fraud detection, to identify criminal activities, the companies can establish better customer relationship and can improve their businesses by analyzing the people buying strategies etc. This paper is going to explain in detail about web usage mining and how it is helpful. Web Usage Mining has seen rapid increase towards research and people communities
Niffler: A Reference Architecture and System Implementation for View Discovery over Pathless Table Collections by Example
Identifying a project-join view (PJ-view) over collections of tables is the
first step of many data management projects, e.g., assembling a dataset to feed
into a business intelligence tool, creating a training dataset to fit a machine
learning model, and more. When the table collections are large and lack join
information--such as when combining databases, or on data lakes--query by
example (QBE) systems can help identify relevant data, but they are designed
under the assumption that join information is available in the schema, and do
not perform well on pathless table collections that do not have join path
information.
We present a reference architecture that explicitly divides the end-to-end
problem of discovering PJ-views over pathless table collections into a human
and a technical problem. We then present Niffler, a system built to address the
technical problem. We introduce algorithms for the main components of Niffler,
including a signal generation component that helps reduce the size of the
candidate views that may be large due to errors and ambiguity in both the data
and input queries. We evaluate Niffler on real datasets to demonstrate the
effectiveness of the new engine in discovering PJ-views over pathless table
collections
Formally analysing the concepts of domestic violence.
The types of police inquiries performed these days are incredibly diverse. Often data processing architectures are not suited to cope with this diversity since most of the case data is still stored as unstructured text. In this paper Formal Concept Analysis (FCA) is showcased for its exploratory data analysis capabilities in discovering domestic violence intelligence from a dataset of unstructured police reports filed with the regional police Amsterdam-Amstelland in the Netherlands. From this data analysis it is shown that FCA can be a powerful instrument to operationally improve policing practice. For one, it is shown that the definition of domestic violence employed by the police is not always as clear as it should be, making it hard to use it effectively for classification purposes. In addition, this paper presents newly discovered knowledge for automatically classifying certain cases as either domestic or non-domestic violence is. Moreover, it provides practical advice for detecting incorrect classifications performed by police officers. A final aspect to be discussed is the problems encountered because of the sometimes unstructured way of working of police officers. The added value of this paper resides in both using FCA for exploratory data analysis, as well as with the application of FCA for the detection of domestic violence.Formal concept analysis (FCA); Domestic violence; Knowledge discovery in databases; Text mining; Exploratory data analysis; Knowledge enrichment; Concept discovery;
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