24 research outputs found
ΠΠ²ΡΠΎΠΌΠ°ΡΠΈΠ·Π°ΡΠΈΡ ΠΏΡΠΎΡΠ΅Π΄ΡΡΡ ΠΎΡΠ΅Π½ΠΊΠΈ ΠΏΠΎ ΠΊΠΎΠΌΠΏΠ΅ΡΠ΅Π½ΡΠΈΡΠΌ ΡΡΡΠ΄Π΅Π½ΡΠΎΠ² ΠΈ ΠΏΡΠ΅ΠΏΠΎΠ΄Π°Π²Π°ΡΠ΅Π»Π΅ΠΉ
Modern Internet technologies integrated into the organization of training activities can significantly expand the educational opportunities of students; are provide choice and implementation of individual learning trajectory. The purpose of this paper is to develop an integrated learning environment for students and teachers, which provides a process for the on-line estimation of competencies. The proposed system implemented as the webapplication "Foundation of Evaluation Tools"
ΠΠ΅ΡΠΎΠ΄Ρ ΡΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ ΠΏΡΠΈΠ±ΡΠ»ΡΡ
ΠΡΠ½ΠΎΠ²Π½ΠΎΠΉ ΡΠ΅Π»ΡΡ ΠΏΡΠ΅Π΄ΠΏΡΠΈΠ½ΠΈΠΌΠ°ΡΠ΅Π»ΡΡΠΊΠΎΠΉ Π΄Π΅ΡΡΠ΅Π»ΡΠ½ΠΎΡΡΠΈ ΡΠ²Π»ΡΠ΅ΡΡΡ ΠΏΡΠΈΠ±ΡΠ»Ρ. ΠΡΠΈΠ±ΡΠ»Ρ Π²ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΡΡ
ΡΡΠ»ΠΎΠ²ΠΈΡΡ
ΡΠ²Π»ΡΠ΅ΡΡΡ ΠΊΠ°ΠΊ ΠΈΡΡΠΎΡΠ½ΠΈΠΊΠΎΠΌ ΡΠ°Π·Π²ΠΈΡΠΈΡ ΠΏΡΠ΅Π΄ΠΏΡΠΈΡΡΠΈΡ, ΡΠ°ΠΊ ΠΈΡΠΎΠ·Π΄Π°Π΅Ρ Π±Π°Π·Ρ Π΄Π»Ρ ΡΠΎΡΡΠ° Π½Π°ΡΠΈΠΎΠ½Π°Π»ΡΠ½ΠΎΠΉ ΡΠΊΠΎΠ½ΠΎΠΌΠΈΠΊΠΈ Π²ΡΠ΅Π»ΠΎΠΌ. ΠΡΠΈΠ±ΡΠ»Ρ ΠΏΡΠ΅Π΄ΠΏΡΠΈΡΡΠΈΡ ΠΏΠ΅ΡΠ΅ΡΠ°ΡΠΏΡΠ΅Π΄Π΅Π»ΡΠ΅ΡΡΡ ΡΠ΅ΡΠ΅Π· Π½Π°Π»ΠΎΠ³ΠΎΠ²ΡΡ ΡΠΈΡΡΠ΅ΠΌΡ, "Π½Π°ΠΏΠΎΠ»Π½ΡΡ" Π³ΠΎΡΡΠ΄Π°ΡΡΡΠ²Π΅Π½Π½ΡΠΉ Π±ΡΠ΄ΠΆΠ΅Ρ Π²ΡΠ΅Ρ
ΡΡΠΎΠ²Π½Π΅ΠΉ. ΠΠΎΡΡΠΎΠΌΡ ΠΎΡΠ²ΠΎΠ΅Π½ΠΈΠ΅ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΠ³ΠΎ ΡΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ ΠΏΡΠΈΠ±ΡΠ»ΡΡ ΠΏΡΠ΅Π΄ΠΏΡΠΈΡΡΠΈΡ ΡΠ²Π»ΡΠ΅ΡΡΡ Π²Π°ΠΆΠ½ΠΎΠΉ ΡΠ΅ΠΌΠΎΠΉ Π½Π° Π΄Π°Π½Π½ΡΠΉ ΠΌΠΎΠΌΠ΅Π½Ρ ΡΠ°Π·Π²ΠΈΡΠΈΡ ΡΡΡΠ°Π½Ρ
Efficient Time and Space Representation of Uncertain Event Data
Process mining is a discipline which concerns the analysis of execution data
of operational processes, the extraction of models from event data, the
measurement of the conformance between event data and normative models, and the
enhancement of all aspects of processes. Most approaches assume that event data
is accurately capture behavior. However, this is not realistic in many
applications: data can contain uncertainty, generated from errors in recording,
imprecise measurements, and other factors. Recently, new methods have been
developed to analyze event data containing uncertainty; these techniques
prominently rely on representing uncertain event data by means of graph-based
models explicitly capturing uncertainty. In this paper, we introduce a new
approach to efficiently calculate a graph representation of the behavior
contained in an uncertain process trace. We present our novel algorithm, prove
its asymptotic time complexity, and show experimental results that highlight
order-of-magnitude performance improvements for the behavior graph
construction.Comment: 34 pages, 16 figures, 5 table