Keven Richly

Identifying the top-dogs of the blogosphere

The development of a resilient weblog ranking metric within the global blogosphere, capable of identifying the most important or influential weblogs around, forms the central aspect of this paper. Because well-established ranking algorithms for traditional web pages are not perfectly applicable to the deviant linking characteristics of the blogosphere, blog engines, such as Technorati, BlogPulse or PostRank have developed their own tailor-made ranking metric. This paper will analyze and compare the ranking criteria of these service providers and reveal their conceptional shortcomings and discuss their strengths. Ultimate objective of this paper is to introduce a novel ranking metric, the so-called “BlogIntelligence-Impact-Score” or “BI-Impact” for short. It represents one of the central informational offerings of the forthcoming “BlogIntelligence” portal.

How surveys, tutors, and software help to assess Scrum adoption in a classroom software engineering project

Agile methods are best taught in a hands-on fashion in realistic projects. The main challenge in doing so is to assess whether students apply the methods correctly without requiring complete supervision throughout the entire project. This paper presents experiences from a classroom project where 38 students developed a single system using a scaled version of Scrum. Surveys helped us to identify which elements of Scrum correlated most with student satisfaction or posed the biggest challenges. These insights were augmented by a team of tutors, which accompanied main meetings throughout the project to provide feedback to the teams, and captured impressions of method application in practice. Finally, we performed a post-hoc, tool-supported analysis of collaboration artifacts to detect concrete indicators for anti-patterns in Scrum adoption. Through the combination of these techniques we were able to understand how students implemented Scrum in this course and which elements require further lecturing and tutoring in future iterations. Automated analysis of collaboration artifacts proved to be a promising addition to the development process that could potentially reduce manual efforts in future courses and allow for more concrete and targeted feedback, as well as more objective assessment.

Optimizing Routes of Public Transportation Systems by Analyzing the Data of Taxi Rides

Public transportation systems are flexible and affordable for the passengers. In contrast, the operation and construction of the necessary infrastructure is cost-intensive and requires extensive planning. Decisions about the scheduling, capacities and the location of stations are dependent on various economic, social, and environmental factors and have a major impact on the structure of a city. In this context, information about the starting points and destinations of potential passengers is highly relevant for operators. Unfortunately, the collection of this data is not trivial and often based on time intensive and expensive studies. In this paper we present a novel approach to gain knowledge for transportation system optimization based on the data of taxi rides, which have been recorded for documentation purposes. This data can be analyzed and offers an insight into the fine-grained travel intentions of millions of people. We introduce an interactive web application, which enables the analysis of about 700 millions taxi rides in New York City. Additionally to the exploration of the most frequent travel routes, the application can automatically suggest useful extensions of the exciting transportation system or suggest an optimized route map, which can be used to evaluate the existing one. With this functionality, the presented software effectively supports the decision processes of operators and enables the continuous evaluation of the existing systems.

Social Physics of the Blogosphere

It was already shown on several occasions that it can be highly meaningful for individuals, institutions or even governments to find ways and measures in order to extract reliable and insightful trends, opinions or partic- ular pieces of information out of the blogosphere. However, it is increasingly difficult if not impossible for the average internet user and sympathizer of we blogs to grasp the blogosphere’s complexity as a whole, due to thousands of new weblogs and an almost uncountable number of new posts adding up to the before-mentioned collective on a daily basis. Mining, analyzing, mod- cling and presenting this vast pool of knowledge in one central framework to extract, exploit and represent meaningful knowledge for the common blog user forms the basis of this paper. The result of the corresponding long-term research initiative presented here is BLOGIXTELLIGEXCE. It is an inte- grated blog analysis framework with the objective to leverage content- and context-related structures and dynamics residing in the blogosphere and to make these findings available in an appropriate format to anyone interested. We hereafter refer to these structures and dynamics as social physics of the blogosphere.

Recognizing Compound Events in Spatio-Temporal Football Data

In the world of football, performance analytics about a player’s skill level and the overall tactics of a match are supportive for the success of a team. These analytics are based on positional data on the one hand and events about the game on the other hand. The positional data of the ball and players is tracked automatically by cameras or via sensors. However, the events are still captured manually by human, which is time-consuming and error-prone. Therefore, this paper introduces an approach to detect events based on the positional data of football matches. We trained and aggregated the machine learning algorithms Support Vector Machine, K-Nearest Neighbors and Random Forest, based on features, which were calculated on base of the positional data. We evaluated the quality of our approach by comparing the recall and precision of the results. This allows an assessment of how event detection in football matches can be improved by automating this process based on spatio-temporal data. We discovered, that it is possible to detect football events from positional data. Nevertheless, the choice of a specific algorithm has a strong influence on the quality of the predicted results.

Incremental, Per-Query Ontology Matching with RepMine

Ontology matching enables applications, such as automated data transformation or query rewriting. As it requires domain knowledge, it needs to be carried out by expert users, whose time is scarce and, therefore, should be used efficiently. To this end, the RepMine system presented in this paper does not treat ontology matching as a task of its own, but integrates it into a semi-automated query translation process. By that, users perform a task with immediate benefit for them and simultaneously contribute to alignments between ontologies. Furthermore, the overall task of matching two ontologies is split on a per-query basis and, thus, can be performed incrementally by all system users

S4J - Integrating SQL into Java at Compiler-Level

Object-oriented languages and relational database systems dominate the design of modern enterprise information systems. However, their interoperability has caused problems ever since. In this paper, we present an approach to integrate SQL into the Java programming language. The integration is done at compiler level, making SQL a first-class citizen of the programming language, including object-awareness, providing validation possibilities that cover e.g. query correctness and type compatibility. In contrast to existing solutions, these validations are carried out during compilation. To evaluate our approach, we implemented a standard business process (Order-To-Pay) using Hibernate, JDBC, and S4J. We compare each implementation in terms of query performance, code size, and code complexity. The evaluation shows that the integration of SQL into Java allows to reduce code size and complexity while maintaining an equal or better performance compared to competitive approaches.

Where is the Money Made? An Interactive Visualization of Profitable Areas in New York City

Detailed information about the flow of potential customers in a city is extremely relevant for strategic decisions of various service providers such as taxi companies or advertising agencies. The knowledge about highly frequented regions as well as peak times in specific areas provides a crucial business advantage to competitors. Today, business relevant decisions about the positioning of service providers and advertising spaces or the balancing of capacity are primarily based on experience only. In this paper, we present a novel approach to gain knowledge about the distribution of potential customers over time and space based on the data of taxi rides, which have been recorded for documentation purposes. By leveraging the performance of in-memory databases, we build an application, which allows the user to analyze about 700 million taxi rides in real-time. The application allows companies to get an impression in which areas and in what timeframes they can reach a large audience of potential customers. Additionally, we demonstrate that the developed visualization concept enables the comparison of different regions and allows to analyze trends in the customer flow over time.

RepMine: A System for Transferrable Analyses of Collaboration Activities in Software Engineering

Vast amounts of knowledge exist about collaboration activities in software engineering and their effects on development projects. Much of this knowledge is captured in research papers, and a subset of it is implemented in collaboration support and analysis tools. The main challenge for the development of these tools is thereby to simultaneously keep up with latest research and changing collaboration infrastructures. If tools are not compatible with recent collaboration tools, or provide outdated analyses, software engineering teams will stop using them. The RepMine system, which we present in this paper, provides a means to store analyses of collaboration activities using a graph-based query abstraction and transfer them to other representations of collaboration data through a semi-automatic ontology matching approach. This decoupling of analyses from data collection reduces the efforts for supporting emerging or changing infrastructures. As the system implements a catalogue for collaboration activity analyses, it can also be readily kept up-to-date with latest research.