Zouhaier Brahmia is currently an Associate Professor of Computer Science in the Department of Computer Science at the Faculty of Economics and Management of the University of Sfax, Tunisia. He is a member of the Multimedia, InfoRmation systems, and Advanced Computing Laboratory (MIRACL). His scientific interests include temporal databases, schema versioning, and temporal, evolution and versioning aspects in emerging databases (XML, and NoSQL), Big Data, and Semantic Web ontologies. He received an MSc degree in Computer Science, in July 2005, and a PhD in Computer Science, in December 2011, from the Faculty of Economics and Management of the University of Sfax.
tauVersioXS Version 1.0 (Version 2.1 is the current development and beta test version) is a proto... more tauVersioXS Version 1.0 (Version 2.1 is the current development and beta test version) is a prototype tool for the management of XML schema versioning in the tauXSchema framework (i.e., versioning of conventional schema and annotation documents). In its current version, \u3c4VersioXS focuses on annotation versioning only. It is part of the research project described in the papers "Schema versioning in tauXSchema-based multitemporal XML repositories" (DOI:10.1109/RCIS.2011.6006845 IRIS hdl:11585/105389). The tauVersioXS tool has been developed in JAVA (Java SE Development Kit 1.7.0) within the Eclipse IDE (eclipse-modeling-luna-SR1-win32-x86_64), on top of MS Windows 7 (64-bit)
Advances in computer and electrical engineering book series, 2019
In information systems, not only do data change over time, but also database schemata evolve freq... more In information systems, not only do data change over time, but also database schemata evolve frequently as a response to evolving application requirements. In the literature, schema evolution and schema versioning are the two techniques that were proposed to support schema changes in a DBMS, without loss of extant data and with continued support of legacy applications. After applying schema changes, schema evolution keeps only the current schema version and retains the data which are adapted to such a schema. On the other hand, each time schema changes are applied, schema versioning creates a new schema version, while preserving old schema versions and their corresponding data. With schema versioning, data access through any schema version is supported, which avoids applications developed with past schemata to become obsolete. The main goal of this chapter is to present the recent research proposals that deal with schema versioning and to discuss the recent advances on schema versioning support in mainstream DBMSs.
Although temporal XML data are being stored and manipulated by several XML-based applications in ... more Although temporal XML data are being stored and manipulated by several XML-based applications in different domains (e.g., e-commerce, e-health), there is neither a temporal XML update language proposed by researchers nor built-in support provided by existing XML DBMSs and tools, for maintaining such data. Furthermore, in the well known temporal XML framework tauXSchema, there are no features for inserting, deleting or updating temporal XML instances. In this paper, we bridge these gaps by proposing a temporal extension of the W3C XQuery Update Facility (XUF) language, named tauXUF (Temporal XUF), which allows manipulating temporal XML data in tauXSchema. With tauXUF both the syntax and the semantics of the update expressions of the XUF language are extended to support temporal aspects. Examples are also provided to motivate and illustrate our proposal.
Several modern applications (e.g., Internet of Things, online social networks), which exploit big... more Several modern applications (e.g., Internet of Things, online social networks), which exploit big data, require a complete history of all changes performed on these data and their schemas (or structures). However, although schema versioning has long been advocated to be the best solution for this issue, currently there are no available technical supports, provided by existing big data management systems (especially NoSQL DBMSs), for handling temporal evolution and versioning aspects of big data. In [14], for a disciplined and systematic approach to the temporal management of JSON-based big data in NoSQL databases, we have proposed the use of a framework, named τJSchema (temporal JSON Schema). It allows defining and validating temporal JSON documents that obey to a temporal JSON schema. A τJSchema schema is composed of a conventional (i.e., non-temporal) JSON schema annotated with a set of temporal logical and temporal physical characteristics. Moreover, since these two components could evolve over time to respond to new applications’ requirements, we have extended τJSchema, in [17], to support versioning of conventional JSON schemas. In this work, we complete the figure by extending our framework to also support versioning of temporal logical and physical characteristics. Indeed, we propose a technique for temporal characteristics versioning, and provide a complete set of low-level change operations for the maintenance of these characteristics; for each operation, we define its arguments and its operational semantics. Thus, with this extension, τJSchema will provide a full support of temporal versioning of JSON-based big data at both instance and schema levels.
Advances in Wireless Technologies and Telecommunication
In the internet of vehicles (IoV) field, blockchain technology has been proposed for durable and ... more In the internet of vehicles (IoV) field, blockchain technology has been proposed for durable and trustworthy bookkeeping of the exchanged data. However, block timestamps assigned by miners are usually delayed with respect to events that generate the stored data, making them unusable for applications dealing with exact timing, like traffic law enforcement and insurance accident investigation. To overcome this shortcoming, the authors propose to add new timestamps to the blockchain, which are assigned by data originators to represent the valid time of data recorded within a transaction. The resulting enhanced blockchain data model, named BiTchain, can be considered from a temporal database perspective as a bitemporal data model. In order to let users and applications enjoy the potential of BiTchain, they also introduce an expressive temporal query language, named BiTEQL, defined as a TSQL2-like temporal extension of the EQL blockchain query language.
Several modern applications (e.g., Internet of Things, online social networks), which exploit big... more Several modern applications (e.g., Internet of Things, online social networks), which exploit big data, require a complete history of all changes performed on these data and their schemas (or structures). However, although schema versioning has long been advocated to be the best solution for this issue, currently there are no available technical supports, provided by existing big data management systems (especially NoSQL DBMSs), for handling temporal evolution and versioning aspects of big data. In [14], for a disciplined and systematic approach to the temporal management of JSON-based big data in NoSQL databases, we have proposed the use of a framework, named τJSchema (temporal JSON Schema). It allows defining and validating temporal JSON documents that obey to a temporal JSON schema. A τJSchema schema is composed of a conventional (i.e., non-temporal) JSON schema annotated with a set of temporal logical and temporal physical characteristics. Moreover, since these two components could evolve over time to respond to new applications’ requirements, we have extended τJSchema, in [17], to support versioning of conventional JSON schemas. In this work, we complete the figure by extending our framework to also support versioning of temporal logical and physical characteristics. Indeed, we propose a technique for temporal characteristics versioning, and provide a complete set of low-level change operations for the maintenance of these characteristics; for each operation, we define its arguments and its operational semantics. Thus, with this extension, τJSchema will provide a full support of temporal versioning of JSON-based big data at both instance and schema levels.
tauVersioXS Version 1.0 (Version 2.1 is the current development and beta test version) is a proto... more tauVersioXS Version 1.0 (Version 2.1 is the current development and beta test version) is a prototype tool for the management of XML schema versioning in the tauXSchema framework (i.e., versioning of conventional schema and annotation documents). In its current version, \u3c4VersioXS focuses on annotation versioning only. It is part of the research project described in the papers "Schema versioning in tauXSchema-based multitemporal XML repositories" (DOI:10.1109/RCIS.2011.6006845 IRIS hdl:11585/105389). The tauVersioXS tool has been developed in JAVA (Java SE Development Kit 1.7.0) within the Eclipse IDE (eclipse-modeling-luna-SR1-win32-x86_64), on top of MS Windows 7 (64-bit)
Advances in computer and electrical engineering book series, 2019
In information systems, not only do data change over time, but also database schemata evolve freq... more In information systems, not only do data change over time, but also database schemata evolve frequently as a response to evolving application requirements. In the literature, schema evolution and schema versioning are the two techniques that were proposed to support schema changes in a DBMS, without loss of extant data and with continued support of legacy applications. After applying schema changes, schema evolution keeps only the current schema version and retains the data which are adapted to such a schema. On the other hand, each time schema changes are applied, schema versioning creates a new schema version, while preserving old schema versions and their corresponding data. With schema versioning, data access through any schema version is supported, which avoids applications developed with past schemata to become obsolete. The main goal of this chapter is to present the recent research proposals that deal with schema versioning and to discuss the recent advances on schema versioning support in mainstream DBMSs.
Although temporal XML data are being stored and manipulated by several XML-based applications in ... more Although temporal XML data are being stored and manipulated by several XML-based applications in different domains (e.g., e-commerce, e-health), there is neither a temporal XML update language proposed by researchers nor built-in support provided by existing XML DBMSs and tools, for maintaining such data. Furthermore, in the well known temporal XML framework tauXSchema, there are no features for inserting, deleting or updating temporal XML instances. In this paper, we bridge these gaps by proposing a temporal extension of the W3C XQuery Update Facility (XUF) language, named tauXUF (Temporal XUF), which allows manipulating temporal XML data in tauXSchema. With tauXUF both the syntax and the semantics of the update expressions of the XUF language are extended to support temporal aspects. Examples are also provided to motivate and illustrate our proposal.
Several modern applications (e.g., Internet of Things, online social networks), which exploit big... more Several modern applications (e.g., Internet of Things, online social networks), which exploit big data, require a complete history of all changes performed on these data and their schemas (or structures). However, although schema versioning has long been advocated to be the best solution for this issue, currently there are no available technical supports, provided by existing big data management systems (especially NoSQL DBMSs), for handling temporal evolution and versioning aspects of big data. In [14], for a disciplined and systematic approach to the temporal management of JSON-based big data in NoSQL databases, we have proposed the use of a framework, named τJSchema (temporal JSON Schema). It allows defining and validating temporal JSON documents that obey to a temporal JSON schema. A τJSchema schema is composed of a conventional (i.e., non-temporal) JSON schema annotated with a set of temporal logical and temporal physical characteristics. Moreover, since these two components could evolve over time to respond to new applications’ requirements, we have extended τJSchema, in [17], to support versioning of conventional JSON schemas. In this work, we complete the figure by extending our framework to also support versioning of temporal logical and physical characteristics. Indeed, we propose a technique for temporal characteristics versioning, and provide a complete set of low-level change operations for the maintenance of these characteristics; for each operation, we define its arguments and its operational semantics. Thus, with this extension, τJSchema will provide a full support of temporal versioning of JSON-based big data at both instance and schema levels.
Advances in Wireless Technologies and Telecommunication
In the internet of vehicles (IoV) field, blockchain technology has been proposed for durable and ... more In the internet of vehicles (IoV) field, blockchain technology has been proposed for durable and trustworthy bookkeeping of the exchanged data. However, block timestamps assigned by miners are usually delayed with respect to events that generate the stored data, making them unusable for applications dealing with exact timing, like traffic law enforcement and insurance accident investigation. To overcome this shortcoming, the authors propose to add new timestamps to the blockchain, which are assigned by data originators to represent the valid time of data recorded within a transaction. The resulting enhanced blockchain data model, named BiTchain, can be considered from a temporal database perspective as a bitemporal data model. In order to let users and applications enjoy the potential of BiTchain, they also introduce an expressive temporal query language, named BiTEQL, defined as a TSQL2-like temporal extension of the EQL blockchain query language.
Several modern applications (e.g., Internet of Things, online social networks), which exploit big... more Several modern applications (e.g., Internet of Things, online social networks), which exploit big data, require a complete history of all changes performed on these data and their schemas (or structures). However, although schema versioning has long been advocated to be the best solution for this issue, currently there are no available technical supports, provided by existing big data management systems (especially NoSQL DBMSs), for handling temporal evolution and versioning aspects of big data. In [14], for a disciplined and systematic approach to the temporal management of JSON-based big data in NoSQL databases, we have proposed the use of a framework, named τJSchema (temporal JSON Schema). It allows defining and validating temporal JSON documents that obey to a temporal JSON schema. A τJSchema schema is composed of a conventional (i.e., non-temporal) JSON schema annotated with a set of temporal logical and temporal physical characteristics. Moreover, since these two components could evolve over time to respond to new applications’ requirements, we have extended τJSchema, in [17], to support versioning of conventional JSON schemas. In this work, we complete the figure by extending our framework to also support versioning of temporal logical and physical characteristics. Indeed, we propose a technique for temporal characteristics versioning, and provide a complete set of low-level change operations for the maintenance of these characteristics; for each operation, we define its arguments and its operational semantics. Thus, with this extension, τJSchema will provide a full support of temporal versioning of JSON-based big data at both instance and schema levels.
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Papers by Zouhaier Brahmia