The Experts below are selected from a list of 4263 Experts worldwide ranked by ideXlab platform
Karol Matiaško - One of the best experts on this subject based on the ideXlab platform.
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Temporal Transaction integrity constraints management
Cluster Computing, 2017Co-Authors: Michal Kvet, Karol MatiaškoAbstract:Data management during the whole life cycle of the object is fundamental requirement of current data processing. Temporal database brings new complex system, which allows storing and handling historical, current and future valid states of the objects. Existing solutions are inadequate in terms of performance—effectiveness, size and time processing requirements of the whole system. This paper deals with the principles of Temporal data modelling, describes the structure and methods for manipulation. It focuses on future valid data processing, which can be processed using the Transactions, too. It deals with Temporal classification rules. Whereas, Temporal system requires extension of the Transaction properties, proposed system, which is fully Temporal, highlights Transaction definition and management. Described solution is mostly designed for communication systems, intelligent transport system, but can manage sensorial data, where performance based on speed and the size of the transmitted data is significant. However, it but can be used in any field due to its versatility.
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uni Temporal modelling extension at the object vs attribute level
European Modelling Symposium, 2013Co-Authors: Michal Kvet, Karol MatiaškoAbstract:Today's requirement for the database systems is to provide management for historical and future valid data. Uni-Temporal system is one of the most frequently used Temporal structure. The time component in this system represents the row state defined validity. Standard uni-Temporal solution used today has a lot of disadvantages, which influences the quality. One of the problems is based on the whole state update, which can generate a lot of duplicities. In addition, it cannot provide management for Transactions. The first part of this paper deals with the modelling structure for Temporal data to improve the performance - size and time consumption. The second one extends the proposed structure, describes the principles, structure and methods for Transaction management. The system is fully implemented and Temporal Transaction oriented.
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EMS - Uni-Temporal Modelling Extension at the Object vs. Attribute Level
2013 European Modelling Symposium, 2013Co-Authors: Michal Kvet, Karol MatiaškoAbstract:Today's requirement for the database systems is to provide management for historical and future valid data. Uni-Temporal system is one of the most frequently used Temporal structure. The time component in this system represents the row state defined validity. Standard uni-Temporal solution used today has a lot of disadvantages, which influences the quality. One of the problems is based on the whole state update, which can generate a lot of duplicities. In addition, it cannot provide management for Transactions. The first part of this paper deals with the modelling structure for Temporal data to improve the performance - size and time consumption. The second one extends the proposed structure, describes the principles, structure and methods for Transaction management. The system is fully implemented and Temporal Transaction oriented.
Hirvonen Kalle - One of the best experts on this subject based on the ideXlab platform.
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Beneficiary views on cash and in-kind payments: Evidence from Ethiopia’s productive safety net programme
Washington DC, 2020Co-Authors: Hirvonen KalleAbstract:Economists often default to the assumption that cash is always preferable to an in-kind transfer. Do beneficiaries feel the same way? This paper addresses this issue using longitudinal household data from Ethiopia where a large-scale social safety net intervention (PSNP) operates. Even though most payments are made in cash, and even though the (Temporal) Transaction costs associated with food payments are higher than payments received as cash, most beneficiaries stated that they prefer their payments only or partly in food. Higher food prices induce shifts in stated preferences towards in-kind transfers. More food secure households, those closer to food markets and to financial services are more likely to prefer cash. Though shifts occur, the stated preference for food is dominant: In no year do more than 17 percent of households prefer only cash. There is suggestive evidence that stated preferences for food are also driven by self-control concerns.IFPRI5; CRP2DSGD; PHND; PIMNon-PRCGIAR Research Program on Policies, Institutions, and Markets (PIM
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Beneficiary views on cash and in-kind payments: Evidence from Ethiopia's Productive Safety Net Programme
'Oxford University Press (OUP)', 2020Co-Authors: Hirvonen KalleAbstract:Economists often default to the assumption that cash is always preferable to an in-kind transfer. Do beneficiaries feel the same way? This paper addresses this issue using longitudinal household data from Ethiopia, where a large-scale social safety net intervention (PSNP) operates. Even though most payments are made in cash, and even though the (Temporal) Transaction costs associated with food payments are higher than payments received as cash, most beneficiaries stated that they prefer their payments only or partly in food. Higher food prices induce shifts in stated preferences toward in-kind transfers. More food-secure households, those closer to food markets and to financial services are more likely to prefer cash. Though shifts occur, the stated preference for food is dominant: In no year do more than 17 percent of households prefer only cash. There is suggestive evidence that stated preferences for food are also driven by self-control concerns.IFPRI3; CRP2; ISI; IFPRIOA; ESSPDSGD; PIMPRCGIAR Research Program on Policies, Institutions, and Markets (PIM
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Payment modality preferences: Evidence from Ethiopia’s Productive Safety Net Programme
Washington DC; Addis Ababa Ethiopia, 2020Co-Authors: Hirvonen KalleAbstract:Economists typically default to the assumption that cash is always preferable to an in-kind transfer. We extend the classic Southworth (1945) framework to predict under what conditions this assumption holds. We take the model to longitudinal household data from Ethiopia where a large-scale social safety net intervention – the Productive Safety Net Programme (PSNP) – operates. Even though most PSNP payments are paid in cash, and even though the (Temporal) Transaction costs associated with food payments are higher than payments received as cash, the overwhelming majority of the beneficiary households prefer their payments only or partly in food. However, these preferences are neither homogeneous nor stable. Higher food prices induce shifts in preferences towards in-kind transfers, but more food secure households and those closer to food markets and to financial services prefer cash. There is suggestive evidence that preferences for food are also driven by self-control concerns.Non-PRIFPRI2; CRP2; ESSPDSGD; PIMCGIAR Research Program on Policies, Institutions, and Markets (PIM
Michal Kvet - One of the best experts on this subject based on the ideXlab platform.
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Temporal Transaction integrity constraints management
Cluster Computing, 2017Co-Authors: Michal Kvet, Karol MatiaškoAbstract:Data management during the whole life cycle of the object is fundamental requirement of current data processing. Temporal database brings new complex system, which allows storing and handling historical, current and future valid states of the objects. Existing solutions are inadequate in terms of performance—effectiveness, size and time processing requirements of the whole system. This paper deals with the principles of Temporal data modelling, describes the structure and methods for manipulation. It focuses on future valid data processing, which can be processed using the Transactions, too. It deals with Temporal classification rules. Whereas, Temporal system requires extension of the Transaction properties, proposed system, which is fully Temporal, highlights Transaction definition and management. Described solution is mostly designed for communication systems, intelligent transport system, but can manage sensorial data, where performance based on speed and the size of the transmitted data is significant. However, it but can be used in any field due to its versatility.
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uni Temporal modelling extension at the object vs attribute level
European Modelling Symposium, 2013Co-Authors: Michal Kvet, Karol MatiaškoAbstract:Today's requirement for the database systems is to provide management for historical and future valid data. Uni-Temporal system is one of the most frequently used Temporal structure. The time component in this system represents the row state defined validity. Standard uni-Temporal solution used today has a lot of disadvantages, which influences the quality. One of the problems is based on the whole state update, which can generate a lot of duplicities. In addition, it cannot provide management for Transactions. The first part of this paper deals with the modelling structure for Temporal data to improve the performance - size and time consumption. The second one extends the proposed structure, describes the principles, structure and methods for Transaction management. The system is fully implemented and Temporal Transaction oriented.
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EMS - Uni-Temporal Modelling Extension at the Object vs. Attribute Level
2013 European Modelling Symposium, 2013Co-Authors: Michal Kvet, Karol MatiaškoAbstract:Today's requirement for the database systems is to provide management for historical and future valid data. Uni-Temporal system is one of the most frequently used Temporal structure. The time component in this system represents the row state defined validity. Standard uni-Temporal solution used today has a lot of disadvantages, which influences the quality. One of the problems is based on the whole state update, which can generate a lot of duplicities. In addition, it cannot provide management for Transactions. The first part of this paper deals with the modelling structure for Temporal data to improve the performance - size and time consumption. The second one extends the proposed structure, describes the principles, structure and methods for Transaction management. The system is fully implemented and Temporal Transaction oriented.
Shianshyong Tseng - One of the best experts on this subject based on the ideXlab platform.
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An incremental mining algorithm for maintaining sequential patterns using pre-large sequences
Expert Systems with Applications, 2011Co-Authors: Tzungpei Hong, Chingyao Wang, Shianshyong TsengAbstract:Highlights? In this paper, we propose an incremental mining algorithm for maintaining sequential patterns based on the concept of pre-large sequences to reduce the need for rescanning original databases. ? The proposed algorithm does not require rescanning original databases until the accumulative amount of newly added customer sequences exceeds a safety bound, which depends on database size. ? The proposed approach becomes increasingly efficient as databases grow. Mining useful information and helpful knowledge from large databases has evolved into an important research area in recent years. Among the classes of knowledge derived, finding sequential patterns in Temporal Transaction databases is very important since it can help model customer behavior. In the past, researchers usually assumed databases were static to simplify data-mining problems. In real-world applications, new Transactions may be added into databases frequently. Designing an efficient and effective mining algorithm that can maintain sequential patterns as a database grows is thus important. In this paper, we propose a novel incremental mining algorithm for maintaining sequential patterns based on the concept of pre-large sequences to reduce the need for rescanning original databases. Pre-large sequences are defined by a lower support threshold and an upper support threshold that act as gaps to avoid the movements of sequences directly from large to small and vice versa. The proposed algorithm does not require rescanning original databases until the accumulative amount of newly added customer sequences exceeds a safety bound, which depends on database size. Thus, as databases grow larger, the numbers of new Transactions allowed before database rescanning is required also grow. The proposed approach thus becomes increasingly efficient as databases grow.
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maintenance of discovered sequential patterns for record deletion
Intelligent Data Analysis, 2002Co-Authors: Chingyao Wang, Tzungpei Hong, Shianshyong TsengAbstract:Mining sequential patterns from Temporal Transaction databases attempts to find customer behavior models and to assist managers in making correct and effective decisions. The sequential patterns discovered may, however, become invalid or inappropriate when databases are updated. Conventional approaches may re-mine entire databases to get correct sequential patterns for maintenance. However, when a database is massive in size, this will require considerable computation time. In the past, Lin and Lee proposed an incremental mining algorithm for maintenance of sequential patterns as new records were inserted. In addition to record insertion, record deletion is also commonly seen in real-world applications. Processing record deletion is, however, different from processing record insertion. The former can even be thought of the contrary of the latter. In this paper, we thus attempt to design an effective maintenance algorithm for sequential patterns as records are deleted. Our proposed algorithm utilizes previously discovered large sequences in the maintenance process, thus reducing numbers of rescanning databases. In addition, rescanning requirement depends on decreased numbers of customers, which are usually zero when numbers of deleted records are not large. This characteristic is especially useful for dynamic database mining.
S. Geetha - One of the best experts on this subject based on the ideXlab platform.
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A Novel Approach to Mine Temporal Association Rules
Data mining and knowledge engineering, 2011Co-Authors: T. Mathu, S. GeethaAbstract:Given a large Temporal Transaction database, the aim of this paper is to discover the itemsets that are having related support during a particular event over time and to find the association between those items. Most works in mining association rules involve the generation of frequent items which is the core process in generating association rules. It is necessary to scan database in each timeslot to generate frequent items in Temporal data mining. This incurs much cost when the number of Transactions is large. In this paper, we propose an approach that utilizes the concept of tight lower and upper bounds of supports at different time intervals. It reduces the number of candidates to be scanned in database. Also our method helps in finding association between items that gets related support when a particular event occurs. Our experimental results proves that the association rules generated from the candidate items are more accurate and incurs less cost than the traditional rule mining methods.
