English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Domains such as scientific workflows and business processes exhibit datamodels with complex relationships between objects. This relationship istypically represented as sequences, where each data item is annotated withmulti-dimensional attributes. There is a need to analyze this data foroperational insights. For example, in business processes, users are interestedin clustering process traces into smaller subsets to discover less complexprocess models. This requires expensive computation of similarity metricsbetween sequence-based data. Related work on dimension reduction and embeddingmethods do not take into account the multi-dimensional attributes of data, anddo not address the interpretability of data in the embedding space (i.e., byfavoring vector-based representation). In this work, we introduce Summarized, aframework for efficient analysis on sequence-based multi-dimensional data usingintuitive and user-controlled summarizations. We introduce summarizationschemes that provide tunable trade-offs between the quality and efficiency ofanalysis tasks and derive an error model for summary-based similarity under anedit-distance constraint. Evaluations using real-world datasets show theeffectives of our framework.

SUMMARIZED: Efficient Framework for Analyzing Multidimensional Process Traces under Edit-distance Constraint