In our understanding, a mind-map is an adaptive engine that basically works incrementally on the fundament of existing transactional streams. Generally, mind-maps consist of symbolic cells that are connected with each other and that become either str
onger or weaker depending on the transactional stream. Based on the underlying biologic principle, these symbolic cells and their connections as well may adaptively survive or die, forming different cell agglomerates of arbitrary size. In this work, we intend to prove mind-maps eligibility following diverse application scenarios, for example being an underlying management system to represent normal and abnormal traffic behaviour in computer networks, supporting the detection of the user behaviour within search engines, or being a hidden communication layer for natural language interaction.
Participating a scientific workshop is nowadays often an adventure because the number of participants do seldom exceed the number of talks. A half-day workshop is mostly finished at lunchtime, speakers are sometimes not present and unexcused, and a s
trict progression of the workshop offers little air for discussion. And when talks are re-scheduled on short notice in case that a speech is dropped out, attaining guests definitely wonder why the presenter is talking about something that does not match the previously announced talk. In this respect, we believe that the organization of a workshop in the classical sense must be reconsidered. It is not enough of compelling the presenters to pay the registration fee only and to let the participants being impassive or taken away mentally. With this work, we address several propositions to become implemented in the future workshop organization. With that, we hope to contribute to the identification of scientific workshops as a place of interaction.
Social Communities in bibliographic databases exist since many years, researchers share common research interests, and work and publish together. A social community may vary in type and size, being fully connected between participating members or eve
n more expressed by a consortium of small and individual members who play individual roles in it. In this work, we focus on social communities inside the bibliographic database DBLP and characterize communities through a simple typifying description model. Generally, we understand a publication as a transaction between the associated authors. The idea therefore is to concern with directed associative relationships among them, to decompose each pattern to its fundamental structure, and to describe the communities by expressive attributes. Finally, we argue that the decomposition supports the management of discovered structures towards the use of adaptive-incremental mind-maps.
Since many years, theoretical concepts of Data Mining have been developed and improved. Data Mining has become applied to many academic and industrial situations, and recently, soundings of public opinion about privacy have been carried out. However,
a consistent and standardized definition is still missing, and the initial explanation given by Frawley et al. has pragmatically often changed over the years. Furthermore, alternative terms like Knowledge Discovery have been conjured and forged, and a necessity of a Data Warehouse has been endeavoured to persuade the users. In this work, we pick up current definitions and introduce an unified definition that covers existing attempted explanations. For this, we appeal to the natural original of chemical states of aggregation.
In this paper, we informally introduce dynamic mind-maps that represent a new approach on the basis of a dynamic construction of connectionist structures during the processing of a data stream. This allows the representation and processing of recursi
vely defined structures and avoids the problem of a more traditional, fixed-size architecture with the processing of input structures of unknown size. For a data stream analysis with association discovery, the incremental analysis of data leads to results on demand. Here, we describe a framework that uses symbolic cells to calculate associations based on transactional data streams as it exists in e.g. bibliographic databases. We follow a natural paradigm of applying simple operations on cells yielding on a mind-map structure that adapts over time.
This Master of Science in Computer and Information Sciences (MICS) is an international accredited master program that has been initiated in 2004 and started in September 2005. MICS is a research-oriented academic study of 4 semesters and a continuati
on of the Bachelor towards the PhD. It is completely taught in English, supported by lecturers coming from more than ten different countries. This report compass a description of its underlying architecture, describes some implementation details and gives a presentation of diverse experiences and results. As the program has been designed and implemented right after the creation of the University, the significance of the program is moreover a self-discovery of the computer science department, which has finally led to the creation of the todays research institutes and research axes.
Documents in scientific newspapers are often marked by attitudes and opinions of the author and/or other persons, who contribute with objective and subjective statements and arguments as well. In this respect, the attitude is often accomplished by a
linguistic modality. As in languages like english, french and german, the modality is expressed by special verbs like can, must, may, etc. and the subjunctive mood, an occurrence of modalities often induces that these verbs take over the role of modality. This is not correct as it is proven that modality is the instrument of the whole sentence where both the adverbs, modal particles, punctuation marks, and the intonation of a sentence contribute. Often, a combination of all these instruments are necessary to express a modality. In this work, we concern with the finding of modal verbs in scientific texts as a pre-step towards the discovery of the attitude of an author. Whereas the input will be an arbitrary text, the output consists of zones representing modalities.
The recognition, involvement, and description of main actors influences the story line of the whole text. This is of higher importance as the text per se represents a flow of words and expressions that once it is read it is lost. In this respect, the
understanding of a text and moreover on how the actor exactly behaves is not only a major concern: as human beings try to store a given input on short-term memory while associating diverse aspects and actors with incidents, the following approach represents a virtual architecture, where collocations are concerned and taken as the associative completion of the actors acting. Once that collocations are discovered, they become managed in separated memory blocks broken down by the actors. As for human beings, the memory blocks refer to associative mind-maps. We then present several priority functions to represent the actual temporal situation inside a mind-map to enable the user to reconstruct the recent events from the discovered temporal results.
Current research in author profiling to discover a legal authors fingerprint does not only follow examinations based on statistical parameters only but include more and more dynamic methods that can learn and that react adaptable to the specific beha
vior of an author. But the question on how to appropriately represent a text is still one of the fundamental tasks, and the problem of which attribute should be used to fingerprint the authors style is still not exactly defined. In this work, we focus on linguistic selection of attributes to fingerprint the style of the authors Parkin, Bassewitz and Leander. We use texts of the genre Fairy Tale as it has a clear style and texts of a shorter size with a straightforward story-line and a simple language.
