Asmaa Saad
Dr. Asmaa Saad is an assistant professor in the Information Systems Department at the Faculty of Computers and Artificial Intelligence, University of Sadat City, Egypt. She works as the deputy director of the Electronic Tests Unit, University of Sadat City. Dr. Asmaa received her B.Sc. degree in 2011, M.Sc. degree in 2016, and Ph.D. in 2022 from the Information Systems Department at the Faculty of Computers and Information, Menoufia University. She is senior member at Scientific Research School of Egypt (SRSEG). Also, she was invited as a reviewer from many international conferences and journals. Dr. Asmaa research interests include Data Mining, Information systems, Data Quality, and Artificial Intelligence.
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affected the economic, social, and health systems around the world. Frequent pattern mining is one of the main research
topics in data stream mining. It is significant in many critical applications, especially in the medical field. This paper
proposes a Compressed Maximal Frequent Pattern based on a Damped Window model over a data stream (CMFP-DW).
Its main contribution is to integrate the concept of correlation with the purpose of finding valuable patterns that are highly
correlated. As such, a new type of pattern is defined, namely the correlated compressed maximal frequent pattern. The
CMFP-DW approach is employed for mining accurate correlated maximal frequent patterns from streaming data, and it
has been validated against a real-world COVID-19 dataset from the healthcare domain. Frequent patterns generated from
this dataset are exploited with the purpose of detecting the COVID-19 cases in different countries of the world. This helps
decision makers take the appropriate precautions to prevent the further spread of the COVID-19 pandemic across the world.
The six experiments carried out show that the proposed approach outperforms two other existing approaches, namely the
estDec and the CP-Tree algorithms regarding accuracy in extracting correlated maximal frequent patterns, memory usage,
and the required response time.
streaming data becomes a pioneer in the field of information
systems. The data stream is a continuous flow of data generated
from different sources. Extracting frequent patterns from
streaming data raises new challenges for the data mining
community. We present an overview of the growing field of data
streams. Many applications handle streaming data such as
sensor networks, traffic management, log data, telephone call
records, and social networks. These applications generate high
volumes of streaming data with velocity, which is difficult to
handle with traditional data mining techniques. This paper
mainly reviewed different research algorithms, scientific
practices, and methods that have been developed for mining
frequent patterns from streaming data. In addition, it discusses
well-known open-source software and tools for data stream
mining, which are developing to handle streaming data. Finally,
it summarizes the open issues and challenges to current existing
approaches while handling and processing data streams in realworld applications.
techniques are proposed for mining accurate conditional functional dependencies rules from such databases to be employed for data cleaning.
The idea of the proposed techniques is to mine firstly maximal closed frequent patterns, then mine the dependable conditional functional dependencies
rules with the help of lift measure. Moreover, data repairing algorithm is proposed for fixing inconsistent tuples found in the database
exploiting the generated rules. An extensive experimental is conducted study to confirm the effectiveness of the proposed techniques compared
with existing technique on both real-life and synthetic medical data sets.
Data mining techniques can be reutilized efficiently in data cleaning process.
Recent studies have shown that databases are often suffered from inconsistent data
issues, which ought to be resolved in the cleaning process. In this paper, we
introduce an automated approach for dependably generating rules from databases
themselves, in order to detect data inconsistency problems from large databases.
The proposed approach employs confidence and lift measures with integrity constraints,
in order to guarantee that generated rules are minimal, non-redundant and
precise. The proposed approach is validated against several datasets from healthcare
domain. We experimentally demonstrate that our approach outperform significant
enhancement over existing approaches.
affected the economic, social, and health systems around the world. Frequent pattern mining is one of the main research
topics in data stream mining. It is significant in many critical applications, especially in the medical field. This paper
proposes a Compressed Maximal Frequent Pattern based on a Damped Window model over a data stream (CMFP-DW).
Its main contribution is to integrate the concept of correlation with the purpose of finding valuable patterns that are highly
correlated. As such, a new type of pattern is defined, namely the correlated compressed maximal frequent pattern. The
CMFP-DW approach is employed for mining accurate correlated maximal frequent patterns from streaming data, and it
has been validated against a real-world COVID-19 dataset from the healthcare domain. Frequent patterns generated from
this dataset are exploited with the purpose of detecting the COVID-19 cases in different countries of the world. This helps
decision makers take the appropriate precautions to prevent the further spread of the COVID-19 pandemic across the world.
The six experiments carried out show that the proposed approach outperforms two other existing approaches, namely the
estDec and the CP-Tree algorithms regarding accuracy in extracting correlated maximal frequent patterns, memory usage,
and the required response time.
streaming data becomes a pioneer in the field of information
systems. The data stream is a continuous flow of data generated
from different sources. Extracting frequent patterns from
streaming data raises new challenges for the data mining
community. We present an overview of the growing field of data
streams. Many applications handle streaming data such as
sensor networks, traffic management, log data, telephone call
records, and social networks. These applications generate high
volumes of streaming data with velocity, which is difficult to
handle with traditional data mining techniques. This paper
mainly reviewed different research algorithms, scientific
practices, and methods that have been developed for mining
frequent patterns from streaming data. In addition, it discusses
well-known open-source software and tools for data stream
mining, which are developing to handle streaming data. Finally,
it summarizes the open issues and challenges to current existing
approaches while handling and processing data streams in realworld applications.
techniques are proposed for mining accurate conditional functional dependencies rules from such databases to be employed for data cleaning.
The idea of the proposed techniques is to mine firstly maximal closed frequent patterns, then mine the dependable conditional functional dependencies
rules with the help of lift measure. Moreover, data repairing algorithm is proposed for fixing inconsistent tuples found in the database
exploiting the generated rules. An extensive experimental is conducted study to confirm the effectiveness of the proposed techniques compared
with existing technique on both real-life and synthetic medical data sets.
Data mining techniques can be reutilized efficiently in data cleaning process.
Recent studies have shown that databases are often suffered from inconsistent data
issues, which ought to be resolved in the cleaning process. In this paper, we
introduce an automated approach for dependably generating rules from databases
themselves, in order to detect data inconsistency problems from large databases.
The proposed approach employs confidence and lift measures with integrity constraints,
in order to guarantee that generated rules are minimal, non-redundant and
precise. The proposed approach is validated against several datasets from healthcare
domain. We experimentally demonstrate that our approach outperform significant
enhancement over existing approaches.