6,854 research outputs found
Knowledge data discovery and data mining in a design environment
Designers, in the process of satisfying design requirements, generally encounter difficulties in, firstly, understanding the problem and secondly, finding a solution [Cross 1998]. Often the process of understanding the problem and developing a feasible solution are developed simultaneously by proposing a solution to gauge the extent to which the solution satisfies the specific requirements. Support for future design activities has long been recognised to exist in the form of past design cases, however the varying degrees of similarity and dissimilarity found between previous and current design requirements and solutions has restrained the effectiveness of utilising past design solutions. The knowledge embedded within past designs provides a source of experience with the potential to be utilised in future developments provided that the ability to structure and manipulate that knowledgecan be made a reality. The importance of providing the ability to manipulate past design knowledge, allows the ranging viewpoints experienced by a designer, during a design process, to be reflected and supported. Data Mining systems are gaining acceptance in several domains but to date remain largely unrecognised in terms of the potential to support design activities. It is the focus of this paper to introduce the functionality possessed within the realm of Data Mining tools, and to evaluate the level of support that may be achieved in manipulating and utilising experiential knowledge to satisfy designers' ranging perspectives throughout a product's development
A Notion of Dynamic Interface for Depth-Bounded Object-Oriented Packages
Programmers using software components have to follow protocols that specify
when it is legal to call particular methods with particular arguments. For
example, one cannot use an iterator over a set once the set has been changed
directly or through another iterator. We formalize the notion of dynamic
package interfaces (DPI), which generalize state-machine interfaces for single
objects, and give an algorithm to statically compute a sound abstraction of a
DPI. States of a DPI represent (unbounded) sets of heap configurations and
edges represent the effects of method calls on the heap. We introduce a novel
heap abstract domain based on depth-bounded systems to deal with potentially
unboundedly many objects and the references among them. We have implemented our
algorithm and show that it is effective in computing representations of common
patterns of package usage, such as relationships between viewer and label,
container and iterator, and JDBC statements and cursors
Leveraging Large Language Models (LLMs) for Process Mining (Technical Report)
This technical report describes the intersection of process mining and large
language models (LLMs), specifically focusing on the abstraction of traditional
and object-centric process mining artifacts into textual format. We introduce
and explore various prompting strategies: direct answering, where the large
language model directly addresses user queries; multi-prompt answering, which
allows the model to incrementally build on the knowledge obtained through a
series of prompts; and the generation of database queries, facilitating the
validation of hypotheses against the original event log.
Our assessment considers two large language models, GPT-4 and Google's Bard,
under various contextual scenarios across all prompting strategies. Results
indicate that these models exhibit a robust understanding of key process mining
abstractions, with notable proficiency in interpreting both declarative and
procedural process models.
In addition, we find that both models demonstrate strong performance in the
object-centric setting, which could significantly propel the advancement of the
object-centric process mining discipline.
Additionally, these models display a noteworthy capacity to evaluate various
concepts of fairness in process mining. This opens the door to more rapid and
efficient assessments of the fairness of process mining event logs, which has
significant implications for the field.
The integration of these large language models into process mining
applications may open new avenues for exploration, innovation, and insight
generation in the field
Concept analysis-based association mining from linked data: A case in industrial decision making
International audienceLinked data (LD) is a rich format increasingly exploited in knowledge discovery from data (KDD). To that end, LD is typically structured as graph, but can also fit the multi-relational data mining (MRDM) paradigm, e.g. as multiple types and object properties may be used in the dataset. Formal concept analysis (FCA) has been successfully used as theoretical framework for KDD in a variety of applications , primely in clustering and association rule mining (ARM) tasks. As FCA applicability to LD is limited by its single data table input format, relational concept analysis (RCA) was introduced as a MRDM extension that successfully deals with links in the data, including cyclic ones. While RCA has been mainly adapted for conceptual clustering in the past, we present here an RCA-based ARM method. It exploits the iterative nature of pattern generation to cut cyclic references with a minimal loss of information. The utility of the rules discovered by our method has been validated by an application as a decision support in the aluminum die casting industry
- …