Mining Sequences of Developer Interactions in Visual Studio for Usage Smells

In this paper, we present a semi-automatic approach for mining a large-scale dataset of IDE interactions to extract usage smells, i.e., inefficient IDE usage patterns exhibited by developers in the field. The approach outlined in this paper first mines frequent IDE usage patterns, filtered via a set of thresholds and by the authors, that are subsequently supported (or disputed) using a developer survey, in order to form usage smells. In contrast with conventional mining of IDE usage data, our approach identifies time-ordered sequences of developer actions that are exhibited by many developers in the field. This pattern mining workflow is resilient to the ample noise present in IDE datasets due to the mix of actions and events that these datasets typically contain. We identify usage patterns and smells that contribute to the understanding of the usability of Visual Studio for debugging, code search, and active file navigation, and, more broadly, to the understanding of developer behavior during these software development activities. Among our findings is the discovery that developers are reluctant to use conditional breakpoints when debugging, due to perceived IDE performance problems as well as due to the lack of error checking in specifying the conditional

A Survey on ML4VIS: Applying Machine Learning Advances to Data Visualization

Inspired by the great success of machine learning (ML), researchers have applied ML techniques to visualizations to achieve a better design, development, and evaluation of visualizations. This branch of studies, known as ML4VIS, is gaining increasing research attention in recent years. To successfully adapt ML techniques for visualizations, a structured understanding of the integration of ML4VISis needed. In this paper, we systematically survey 88 ML4VIS studies, aiming to answer two motivating questions: "what visualization processes can be assisted by ML?" and "how ML techniques can be used to solve visualization problems?" This survey reveals seven main processes where the employment of ML techniques can benefit visualizations:Data Processing4VIS, Data-VIS Mapping, InsightCommunication, Style Imitation, VIS Interaction, VIS Reading, and User Profiling. The seven processes are related to existing visualization theoretical models in an ML4VIS pipeline, aiming to illuminate the role of ML-assisted visualization in general visualizations.Meanwhile, the seven processes are mapped into main learning tasks in ML to align the capabilities of ML with the needs in visualization. Current practices and future opportunities of ML4VIS are discussed in the context of the ML4VIS pipeline and the ML-VIS mapping. While more studies are still needed in the area of ML4VIS, we hope this paper can provide a stepping-stone for future exploration. A web-based interactive browser of this survey is available at https://ml4vis.github.ioComment: 19 pages, 12 figures, 4 table

Deep Learning Data and Indexes in a Database

A database is used to store and retrieve data, which is a critical component for any software application. Databases requires configuration for efficiency, however, there are tens of configuration parameters. It is a challenging task to manually configure a database. Furthermore, a database must be reconfigured on a regular basis to keep up with newer data and workload. The goal of this thesis is to use the query workload history to autonomously configure the database and improve its performance. We achieve proposed work in four stages: (i) we develop an index recommender using deep reinforcement learning for a standalone database. We evaluated the effectiveness of our algorithm by comparing with several state-of-the-art approaches, (ii) we build a real-time index recommender that can, in real-time, dynamically create and remove indexes for better performance in response to sudden changes in the query workload, (iii) we develop a database advisor. Our advisor framework will be able to learn latent patterns from a workload. It is able to enhance a query, recommend interesting queries, and summarize a workload, (iv) we developed LinkSocial, a fast, scalable, and accurate framework to gain deeper insights from heterogeneous data

(So) Big Data and the transformation of the city

The exponential increase in the availability of large-scale mobility data has fueled the vision of smart cities that will transform our lives. The truth is that we have just scratched the surface of the research challenges that should be tackled in order to make this vision a reality. Consequently, there is an increasing interest among different research communities (ranging from civil engineering to computer science) and industrial stakeholders in building knowledge discovery pipelines over such data sources. At the same time, this widespread data availability also raises privacy issues that must be considered by both industrial and academic stakeholders. In this paper, we provide a wide perspective on the role that big data have in reshaping cities. The paper covers the main aspects of urban data analytics, focusing on privacy issues, algorithms, applications and services, and georeferenced data from social media. In discussing these aspects, we leverage, as concrete examples and case studies of urban data science tools, the results obtained in the “City of Citizens” thematic area of the Horizon 2020 SoBigData initiative, which includes a virtual research environment with mobility datasets and urban analytics methods developed by several institutions around Europe. We conclude the paper outlining the main research challenges that urban data science has yet to address in order to help make the smart city vision a reality

Using contextual information to understand searching and browsing behavior

There is great imbalance in the richness of information on the web and the succinctness and poverty of search requests of web users, making their queries only a partial description of the underlying complex information needs. Finding ways to better leverage contextual information and make search context-aware holds the promise to dramatically improve the search experience of users. We conducted a series of studies to discover, model and utilize contextual information in order to understand and improve users' searching and browsing behavior on the web. Our results capture important aspects of context under the realistic conditions of different online search services, aiming to ensure that our scientific insights and solutions transfer to the operational settings of real world applications

Unusual event detection in real-world surveillance applications

Given the near-ubiquity of CCTV, there is significant ongoing research effort to apply image and video analysis methods together with machine learning techniques towards autonomous analysis of such data sources. However, traditional approaches to scene understanding remain dependent on training based on human annotations that need to be provided for every camera sensor. In this thesis, we propose an unusual event detection and classification approach which is applicable to real-world visual monitoring applications. The goal is to infer the usual behaviours in the scene and to judge the normality of the scene on the basis on the model created. The first requirement for the system is that it should not demand annotated data to train the system. Annotation of the data is a laborious task, and it is not feasible in practice to annotate video data for each camera as an initial stage of event detection. Furthermore, even obtaining training examples for the unusual event class is challenging due to the rarity of such events in video data. Another requirement for the system is online generation of results. In surveillance applications, it is essential to generate real-time results to allow a swift response by a security operator to prevent harmful consequences of unusual and antisocial events. The online learning capabilities also mean that the model can be continuously updated to accommodate natural changes in the environment. The third requirement for the system is the ability to run the process indefinitely. The mentioned requirements are necessary for real-world surveillance applications and the approaches that conform to these requirements need to be investigated. This thesis investigates unusual event detection methods that conform with real-world requirements and investigates the issue through theoretical and experimental study of machine learning and computer vision algorithms