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Found 13 papers, 5 papers with code
PoWareMatch: a Quality-aware Deep Learning Approach to Improve Human Schema Matching
Schema matching is a core task of any data integration process.
FlexER: Flexible Entity Resolution for Multiple Intents
In what follows, we introduce the problem of multiple intents entity resolution (MIER), an extension to the universal (single intent) entity resolution task.
Discovering Hierarchical Processes Using Flexible Activity Trees for Event Abstraction
In this paper, we propose FlexHMiner, a three-step approach to discover processes with multi-level interleaved subprocesses.
The Battleship Approach to the Low Resource Entity Matching Problem
Entity matching, a core data integration problem, is the task of deciding whether two data tuples refer to the same real-world entity.
Query Scheduling in the Presence of Complex User Profiles
Advances in Web technology enable personalization proxies that assist users in satisfying their complex information monitoring and aggregation needs through the repeated querying of multiple volatile data sources.
Learning to Characterize Matching Experts
Matching is a task at the heart of any data integration process, aimed at identifying correspondences among data elements.
Mapping Patterns for Virtual Knowledge Graphs
Virtual Knowledge Graphs (VKG) constitute one of the most promising paradigms for integrating and accessing legacy data sources.
Uncertain Process Data with Probabilistic Knowledge: Problem Characterization and Challenges
The first includes conformance checking of a stochastically known log with respect to a given process model.
AI-Augmented Business Process Management Systems: A Research Manifesto
AI-Augmented Business Process Management Systems (ABPMSs) are an emerging class of process-aware information systems, empowered by trustworthy AI technology.
Conformance Checking Over Stochastically Known Logs
We envision the proposed stochastic conformance checking approach as a viable process mining component for future analysis of stochastic event logs.
From Limited Annotated Raw Material Data to Quality Production Data: A Case Study in the Milk Industry (Technical Report)
In this paper we propose a design methodology, using active learning to enhance learning capabilities, for building a model of production outcome using a constrained amount of raw material training data.
Human's Role in-the-Loop
Given the availability of data and the improvement of machine learning techniques, this blog discusses the respective roles of humans and machines in achieving cognitive tasks in matching, aiming to determine whether traditional roles of humans and machines are subject to change.
SKTR: Trace Recovery from Stochastically Known Logs
Developments in machine learning together with the increasing usage of sensor data challenge the reliance on deterministic logs, requiring new process mining solutions for uncertain, and in particular stochastically known, logs.
