Adam Grzywaczewski

E-Marketing Strategy for Businesses

The Internet provides an easy and uniform way for businesses to make their brands and products visible to their customers. Due to the vast number of companies that take advantage of the Internet to conduct their business, it is very challenging for them to increase their sales and market awareness. In this paper, we investigate two techniques which are used to improve the visibility of an e-business in order to generate more traffic and sales. We describe a process of capturing the Return on Investment (ROI) from Search Engine Marketing (SEM) and focus on two main techniques: Search Engine Optimization (SEO) and Pay Per Click campaign (PPC). The investigation is carried out based on two UK-based small and medium sized enterprises (SME). We describe the results of optimization and its impact on the companies’ future strategy in this paper.

Design implications for task-specific search utilities for retrieval and re-engineering of code

ABSTRACT The importance of information retrieval systems is unquestionable in the modern society and both individuals as well as enterprises recognise the benefits of being able to find information effectively. Current code-focused information retrieval systems such as Google Code Search, Codeplex or Koders produce results based on specific keywords. However, these systems do not take into account developers’ context such as development language, technology framework, goal of the project, project complexity and developer’s domain expertise. They also impose additional cognitive burden on users in switching between different interfaces and clicking through to find the relevant code. Hence, they are not used by software developers. In this paper, we discuss how software engineers interact with information and general-purpose information retrieval systems (e.g. Google, Yahoo!) and investigate to what extent domain-specific search and recommendation utilities can be developed in order to support their work-related activities. In order to investigate this, we conducted a user study and found that software engineers followed many identifiable and repeatable work tasks and behaviours. These behaviours can be used to develop implicit relevance feedback-based systems based on the observed retention actions. Moreover, we discuss the implications for the development of task-specific search and collaborative recommendation utilities embedded with the Google standard search engine and Microsoft IntelliSense for retrieval and re-engineering of code. Based on implicit relevance feedback, we have implemented a prototype of the proposed collaborative recommendation system, which was evaluated in a controlled environment simulating the real-world situation of professional software engineers. The evaluation has achieved promising initial results on the precision and recall performance of the system.

Investigating the value of retention actions as a source of relevance information in the software development environment

Even though there exists a number of search solutions targetted at software engineers the literature suggests that they are not widely used by the people engaged in code delivery [26]. Moreover, current code focused information retrieval systems such as Google Code Search (discontinued), Codeplex or Koders produce results based on specific keywords and therefore they do not take into account user context such as location, browsing history, previous interaction patterns and domain expertise. In this paper we discuss the development of task-specific information retrieval systems for software engineers. We discuss how software engineers interact with information and information retrieval systems and investigate to what extent a domain-specific search and recommendation system can be developed in order to support their work related activities. We have conducted a user study: a questionnaire and an automated observation of user interactions with the browser and software development environment. We discuss factors that can be used as implicit feedback indicators for further collaborative filtering and discuss how these parameters can be analysed using Computational Intelligence based techniques.

A novel Big Data analytics and intelligent technique to predict driver's intent

Abstract Modern age offers a great potential for automatically predicting the drivers intent through the increasing miniaturization of computing technologies, rapid advancements in communication technologies and continuous connectivity of heterogeneous smart objects. Inside the cabin and engine of modern cars, dedicated computer systems need to possess the ability to exploit the wealth of information generated by heterogeneous data sources with different contextual and conceptual representations. Processing and utilizing this diverse and voluminous data, involves many challenges concerning the design of the computational technique used to perform this task. In this paper, we investigate the various data sources available in the car and the surrounding environment, which can be utilized as inputs in order to predict drivers intent and behavior. As part of investigating these potential data sources, we conducted experiments on e-calendars for a large number of employees, and have reviewed a number of available geo referencing systems. Through the results of a statistical analysis and by computing location recognition accuracy results, we explored in detail the potential utilization of calendar location data to detect the drivers intentions. In order to exploit the numerous diverse data inputs available in modern vehicles, we investigate the suitability of different Computational Intelligence (CI) techniques, and propose a novel fuzzy computational modelling methodology. Finally, we outline the impact of applying advanced CI and Big Data analytics techniques in modern vehicles on the driver and society in general, and discuss ethical and legal issues arising from the deployment of intelligent self-learning cars.

Supporting information exchange among software developers through the development of Collaborative Information Retrieval utilities

Software developers produce a significant amount of knowledge, everyday facing a significant amount of engineering challenges and resolving them using a significant number of information resources. Once the task that they are facing is complete and the results of their effort are embedded into the source code this knowledge is very rarely shared with the software development community. As a consequence other software developers when faced with the same or similar problem have to solve it by themselves wasting a significant amount of time and resources every day. This paper proposes a Collaborative Information Recommender (CIR) system which intends to address the problem outlined above and facilitate the exchange of information between software developers. The proposed CIR system not only supports a direct reuse of the code related information found by other developers but also supports the creation and management of very problem focused social and knowledge networks.

Design Challenges and Solutions: Review of the 4th International Workshop on Ubiquitous Computing (iUBICOM 2009)

This paper provides an overview of several approaches, methods and techniques, of ubiquitous and collaborative computing, discussed in the papers submitted to the International Workshop on Ubiquitous Computing (iUBICOM-09). In this workshop, we aimed to balance discussion of technological factors with human aspects in order to explore implications for better design. The theme was information retrieval, decision making processes, and user needs in the context of ubiquitous computing. This paper includes work carried out on different dimensions focusing on technological as well as social aspects of ubiquitous and collaborative computing.