This article explores the impact of source data compression on the performance of task execution at the receiver side of a communication system, and investigates the interference and impact of channel environment variations on semantic coding features. In order to further optimize the performance of the semantic communication model, a semantic communication framework (DIB-DeepSC) based on disentangled information bottleneck is proposed, which improves the inference accuracy of the model by separating and decoupling irrelevant information in the source data, thus compressing valid information related to downstream task execution to a greater extent, reduced communication overhead. Meanwhile, the influence of the Lagrange multiplier$\beta $in the classical information bottleneck (IB) framework is eliminated, which avoids the need to manually optimize$\beta $several times in the semantic communication model. And the dynamic coding method (DIB-DE) is further designed based on adaptive weights, which can dynamically adjust the coding features according to the channel conditions and enhance the robustness of the model. Numerous experiments show that the proposed DIB-DeepSC framework combined with the DIB-DE dynamic encoding communication scheme possesses better semantic extraction and task inference performance relative to the benchmark methods. This scheme is expected to realize more efficient and reliable semantic transmission in practical communication systems and provides new ideas for developing practical semantic communication systems.

Maintaining timeliness and data freshness for real-time data objects has long been recognized as an important problem in real-time database research. Despite years of active research, most of the past work focuses on uniprocessor systems. In this paper, we study the workload-aware temporal consistency maintenance problem upon multiprocessor platforms. We consider the problem of how to partition a set of update transactions to$m \ge 2$processors to maintain the temporal consistency of real-time data objects under both earliest deadline first (EDF) and deadline monotonic (DM) scheduling in each processor, while minimizing the total workload on$m$processors. Firstly, we only consider the feasibility aspect of the problem by proposing two polynomial time partitioning schemes, Temporal Consistency Partitioning under EDF ($\mathsf TCP_{\mathsf{EDF}}$) and Temporal Consistency Partitioning under DM ($\mathsf TCP_{\mathsf{DM}}$), and formally showing that the resource augmentation bounds of both$\mathsf TCP_{\mathsf{EDF}}$and$\mathsf TCP_{\mathsf{DM}}$are$({3 - \frac{1}{m}})$. Secondly, we address the partition problem globally by proposing a polynomial time heuristic, Density factor Balancing Fit ($\mathsf{DBF}$), where density factor balancing plays a major role in producing workload-efficient partitionings. Finally, we evaluate the feasibility and workload performances of$\mathsf{DBF}$versus other heuristics with comparable quality experimentally.

Maintaining the freshness of real-time data is one of the crucial design issues in cyber-physical systems (CPS). Past studies have focused on designing update algorithms to minimize the workload imposed by a fixed set of update tasks while ensuring the temporal validity of data. In this paper, we revisit this problem in dynamic cyber-physical systems (DCPS) which may exhibit multi-modal behavior. Any solution to this problem must recognize that: (1) different update algorithms may be needed in different modes according to the workload in each mode, and (2) temporal validity of data must be maintained not only in each mode but also during the mode switch. To strike a balance between data freshness and system schedulability, we propose a utilization-based scheduling selection (UBSS) strategy. We first introduce two synchronous mode switch algorithms, named search-based switch (SBS) and adjustment-based switch (ABS) to search for the proper switch point online and execute all update tasks in the new mode synchronously. SBS checks for temporal validity at the beginning time slot of each idle period in the schedule, while ABS relaxes this restriction through schedule adjustment. To support immediate mode switch, we propose an asynchronous switch algorithm named instant switch (IS) to reduce the switch delay. IS schedules outstanding jobs from the old mode together with the jobs in the new mode using the least-available-laxity-first scheduling policy. Our experimental results demonstrate the effectiveness of these three algorithms. They also show that UBSS strategy can significantly outperform a single fixed update algorithm in terms of maintaining better data freshness while incurring only limited online switch overhead.

Although the deferrable scheduling algorithm for fixed priority transactions ( DS-FP) has been shown to provide a better performance compared with the More-Less (ML) method, there is still a lack of any comprehensive studies on the necessary and sufficient conditions for the schedulability of DS-FP. In this paper, we first analyze the necessary and sufficient schedulability conditions for DS-FP, and then propose a schedulability test algorithm for DS-FP by exploiting the fact that there always exists a repeating pattern in a DS-FP schedule. To resolve the limitation of fixed priority scheduling in DS-FP, we then extend the deferrable scheduling to a dynamic priority scheduling algorithm called DS-EDF by applying the earliest deadline first (EDF) policy to schedule update jobs. We also propose a schedulability test for DS-EDF and compare its performance with DS-FP and ML through extensive simulation experiments. The results show that the schedulability tests are effective. Although the schedulability of DS-EDF is lower than DS-FP and the repeating patterns in DS-EDF schedules are longer than those in DS-FP due to the use of dynamic priority scheduling, the performance of DS-EDF is better than both DS-FP and ML in terms of CPU utilization and impact on lower priority application transactions.

Deriving deadlines and periods for update transactions so as to maintain timeliness and data freshness while minimizing imposed workload has long been recognized an important problem in real-time database research. Despite years of active research, the state-of-the-art still has much room for improvement, particularly for periodic transactions scheduled by the Earliest Deadline First (EDF) algorithm. In this paper, we propose a practical and efficient two-phase algorithm, GEneral EDF (GEEDF), for assigning periods and deadlines to EDF-scheduled update transactions. Phase 1 of GEEDFaims at finding solutions for most inputs in linear time, based on the observation that the execution times of update transactions are relatively small compared to the validity interval lengths of real-time data objects in many real-time applications. In the remaining cases for which Phase 1 fails to derive solutions, Phase 2 is invoked by employing an existing deadline-monotonic-based algorithm, which we show is also applicable to our problem. Meanwhile, we have devised several techniques which significantly reduce the cost of schedulability test, and hence greatly improve time efficiency. Our experimental results demonstrate that GEEDFoutperforms existing approaches in terms of generated workloads. Although Phase 2 has a pseudopolynomial time complexity, our experimental study shows that it runs much faster than other solutions with comparable quality.

In many telecom and web applications, there is a need to identify whether data objects in the same source or different sources represent the same entity in the real-world. This problem arises for subscribers in multiple services, customers in supply chain management, and users in social networks when there lacks a unique identifier across multiple data sources to represent a real-world entity. Entity resolution is to identify and discover objects in the data sets that refer to the same entity in the real world. We investigate the entity resolution problem for large data sets where efficient and scalable solutions are needed. We propose a novel unsupervised blocking algorithm, namely SPectrAl Neighborhood (SPAN), which constructs a fast bipartition tree for the records based on spectral clustering such that real entities can be identified accurately by neighborhood records in the tree. There are two major novel aspects in our approach: 1)We develop a fast algorithm that performs spectral clustering without computing pairwise similarities explicitly, which dramatically improves the scalability of the standard spectral clustering algorithm; 2) We utilize a stopping criterion specified by Newman-Girvan modularity in the bipartition process. Our experimental results with both synthetic and real-world data demonstrate that SPAN is robust and outperforms other blocking algorithms in terms of accuracy while it is efficient and scalable to deal with large data sets.

Deriving deadlines and periods of update transactions for maintaining timeliness and data freshness has long been recognized as an important problem in real-time database research. Despite years of active research, the state of the art only focuses on uniprocessor systems. In this paper, we take a first step of studying the workload-aware temporal consistency maintenance problem upon multiprocessor platforms. We consider the problem of how to partition a set of update transactions to m ≥ 2 processors to maintain the temporal consistency of real-time data objects under earliest deadline first (EDF) scheduling, while minimizing the total workload on m processors. Firstly, we only consider the feasibility aspect of the problem by proposing a polynomial time partitioning scheme, Temporal Consistency Partitioning (TCP), and formally showing that the resource augmentation bound of TCP is (3 - 1/m). Secondly, we address the partition problem globally by proposing a polynomial time heuristic, Density factor Balancing Fit (DBF), where density factor balancing plays a major role in producing workload-efficient partitionings. Finally, we evaluate the feasibility and workload performances of DBF versus other heuristics with comparable quality experimentally.

This paper investigates the discovery of conditional functional dependencies (CFDs). CFDs are a recent extension of functional dependencies (FDs) by supporting patterns of semantically related constants, and can be used as rules for cleaning relational data. However, finding quality CFDs is an expensive process that involves intensive manual effort. To effectively identify data cleaning rules, we develop techniques for discovering CFDs from relations. Already hard for traditional FDs, the discovery problem is more difficult for CFDs. Indeed, mining patterns in CFDs introduces new challenges. We provide three methods for CFD discovery. The first, referred to as CFDMiner, is based on techniques for mining closed item sets, and is used to discover constant CFDs, namely, CFDs with constant patterns only. Constant CFDs are particularly important for object identification, which is essential to data cleaning and data integration. The other two algorithms are developed for discovering general CFDs. One algorithm, referred to as CTANE, is a levelwise algorithm that extends TANE, a well-known algorithm for mining FDs. The other, referred to as FastCFD, is based on the depth-first approach used in FastFD, a method for discovering FDs. It leverages closed-item-set mining to reduce the search space. As verified by our experimental study, CFDMiner can be multiple orders of magnitude faster than CTANE and FastCFD for constant CFD discovery. CTANE works well when a given relation is large, but it does not scale well with the arity of the relation. FastCFD is far more efficient than CTANE when the arity of the relation is large; better still, leveraging optimization based on closed-item-set mining, FastCFD also scales well with the size of the relation. These algorithms provide a set of cleaning-rule discovery tools for users to choose for different applications.

This paper investigates the discovery of conditional functional dependencies (CFDs). CFDs are a recent extension of functional dependencies (FDs) by supporting patterns of semantically related constants, and can be used as rules for cleaning relational data. However, finding CFDs is an expensive process that involves intensive manual effort. To effectively identify data cleaning rules, we develop techniques for discovering CFDs from sample relations. We provide three methods for CFD discovery. The first, referred to as CFDMiner, is based on techniques for mining closed itemsets, and is used to discover constant CFDs, namely, CFDs with constant patterns only. The other two algorithms are developed for discovering general CFDs. The first algorithm, referred to as CTANE, is a levelwise algorithm that extends TANE, a well-known algorithm for mining FDs. The other, referred to as FastCFD, is based on the depthfirst approach used in FastFD, a method for discovering FDs. It leverages closed-itemset mining to reduce search space. Our experimental results demonstrate the following. (a) CFDMiner can be multiple orders of magnitude faster than CTANE and FastCFD for constant CFD discovery. (b) CTANE works well when a given sample relation is large, but it does not scale well with the arity of the relation. (c) FastCFD is far more efficient than CTANE when the arity of the relation is large.

Maintaining the temporal validity of real-time data is one of the crucial issues in a real-time database system. Past studies focus on designing algorithms to minimize imposed workload by a fixed set of update transactions while maintaining data freshness within validity intervals. In this paper we revisit this problem by investigating the cost of data freshness maintenance and online scheduling overhead in the presence of mode changes in real-time systems. We propose to apply periodic scheduling policies when the imposed update workload is low to maintain high data freshness. When the update workload becomes high, we propose to switch to more sophisticated algorithms to improve schedulability. In the latter case, not only each scheduling policy must be able to schedule the task set in the corresponding mode, temporal validity must also be maintained during the mode changes. To address this problem, two algorithms, named search-based switch (SBS) and adjustment-based switch (ABS) are proposed to search for the proper switch point online. SBS checks the temporal validity at the beginning time slot of each idle period while ABS further relaxes this restriction through schedule adjustment. Our experimental results demonstrate the correctness and efficiency of these two algorithms. Our results also show that scheduling switch according to the runtime processor workload can significantly outperform a single fixed scheduling policy in terms of data freshness while incurring only limited online switch overhead.

The schedulability testing for the deferrable scheduling algorithm for fixed priority transactions (DS-FP) remainsan open problem since its introduction. In this paper, wetake the first step towards investigating necessary and sufficient conditions for the DS-FP schedulability. We propose a necessary and sufficient schedulability condition for the algorithm in discrete time systems, and prove its correctness. Based on this condition, we propose a schedulability test algorithm that is more accurate than the existing test that is only based on a sufficient condition. Our algorithm exploits the fact that there is always a repeating pattern in a DS-FP schedule in discrete time systems. We demonstrate through examples that our schedulability test algorithm outperforms the existing algorithm in terms of accuracy.

The periodic update transaction model has been used to maintain the freshness (or temporal validity) of real-time data. Period and deadline assignment has been the main focus of past studies, such as the More-Less scheme [25], in which update transactions are guaranteed by the Deadline Monotonic scheduling algorithm [16] to complete by their deadlines. In this paper, we propose a deferrable scheduling algorithm for fixed-priority transactions, a novel approach for minimizing update workload while maintaining the temporal validity of real-time data. In contrast to prior work on maintaining data freshness periodically, update transactions follow an aperiodic task model in the deferrable scheduling algorithm. The deferrable scheduling algorithm exploits the semantics of temporal validity constraint of real-time data by judiciously deferring the sampling times of update transaction jobs as late as possible. We present a theoretical estimation of its processor utilization and a sufficient condition for its schedulability. Our experimental results verify the theoretical estimation of the processor utilization. We demonstrate through the experiments that the deferrable scheduling algorithm is an effective approach and it significantly outperforms the More-Less scheme in terms of reducing processor workload.

As quality of service (QoS)-enabled component middleware technologies gain widespread acceptance to build distributed real-time and embedded (DRE) systems, it becomes necessary for these technologies to support real-time publish/subscribe services, which is a key requirement of a large class of DRE systems. To date there have been very limited systematic studies evaluating different approaches to integrating real-time publish/subscribe services in QoS-enabled component middleware. This paper makes two contributions in addressing these key research questions. First, we evaluate the pros and cons of three different design alternatives for integrating publish/subscribe services within QoS-enabled component middleware. Second, we empirically evaluate the performance of our container-based design and compare it with mature object-oriented real-time publish/subscribe implementations. Our studies reveal that both the performance and scalability of our design and implementation are comparable to its object-oriented counterpart, which provides a key guidance to the suitability of component technologies for DRE systems.

The deferrable scheduling algorithm for fixed priority transactions (DS-FP) is demonstrated to be a very effective approach for minimizing real-time update transaction workload while maintaining temporal validity of real-time data. This paper examines the schedulability of deferrable scheduling and presents a sufficient condition. While its online scheduling overhead is a concern, we propose a hyperperiod based DS-FP approach satisfying the temporal validity constraint with low overhead. The algorithm, namely DEferrable Scheduling with Hyperperiod by Schedule Adjustment (DESH-SA), adjusts the DS-FP schedule in an interval so that the adjusted schedule in the interval can be repeated infinitely. Our experiments demonstrate that DESH-SA can reduce scheduling overhead of DS-FP and also achieve update workload near optimal

A real-time database is composed of real-time objects whose values remain valid only within their validity intervals. Each object in the database models a real world entity. The freshness of these objects is maintained by update transactions that sample the real world entities. The literature proposes various ways to derive a schedule of transactions that preserves the freshness (also known as absolute consistency) of these objects. But these approaches do not take care of the mutual consistency of the objects, i.e., whether together they represent a logical state of the system. We investigate the problem of checking whether, given an update transaction schedule, a periodic query would be able to read mutually consistent values. We propose solutions for both single- and multiple-query cases in the presence of non-preemptable query executions. Specifically, we first investigate formulas that give the maximal value of mutual gaps among a set of data read at a certain point in time. (A mutual gap for two object values read from the database refers to the difference between the times at which the two objects were updated.) We then propose design approaches to (1) decide the period and relative deadline of a query so that it would guarantee mutual consistency; (2) decide if a given set of queries with relative deadlines and periods can guarantee mutual consistency. Finally, we suggest ways of reducing the complexity of our proposed approaches for both harmonic periods and general cases

The more-less (ML) scheme has been shown to be an efficient approach for maintaining temporal consistency of real-time data objects. Although ML provides a deterministic guarantee in temporal consistency, the number of update transactions that can be supported in a system is limited. This is due to its use of the worst-case computation time in deriving deadlines and periods of update transactions. This paper studies the problem of temporal consistency maintenance where a certain degree of temporal inconsistency is tolerable. A suite of statistical more-less (SML) approaches are proposed to explore the trade-off between quality of service (QoS) of temporal consistency and the number of supported transactions. It begins with a baseline algorithm, SML-BA, which provides the requested QoS of temporal consistency. Then, SML with optimization (SML-OPT) is proposed to further improve the QoS by better utilizing the excess processor capacity. Finally, SML-OPT is enhanced with a slack reclaiming scheme (SML-SR). The reclaimed slacks are used to process jobs whose required computation time is larger than the guaranteed computation time. Simulation experiments are conducted to compare the performance of these schemes (SML-BA, SML-OPT, and SML-SR) together with the deterministic more-less and half-half schemes. The results show that the SML schemes are effective in trading the schedulability of transactions for the QoS guaranteed. Moreover, SML-SR performs best and offers a significant QoS improvement over SML-BA and SML-OPT.

Periodic update transaction model has been used to maintain freshness (or temporal validity) of real-time data. Period and deadline assignment has been the main focus in the past studies such as the more-less scheme by Xiong and Ramamrithan (2004) in which update transactions are guaranteed by the deadline monotonic scheduling algorithm by Leung and Whitehead (1982) to complete by their deadlines. In this paper, we propose a novel algorithm, namely deferrable scheduling, for minimizing imposed workload while maintaining temporal validity of real-time data. In contrast to previous work, update transactions scheduled by the deferrable scheduling algorithm follow a sporadic task model. The deferrable scheduling algorithm exploits the semantics of temporal validity constraint of real-time data by judiciously deferring the sampling times of update transaction jobs as late as possible. We present a theoretical analysis of its processor utilization, which is verified in our experiments. Our experimental results also demonstrate that the deferrable scheduling algorithm is a very effective approach, and it significantly outperforms the more-less scheme in terms of reducing processor workload

The proliferation of XML as a standard for data representation and exchange in diverse, next-generation Web applications has created an emphatic need for effective XML data-integration tools. For several real-life scenarios, such XML data integration needs to be DTD-directed -- in other words, the target, integrated XML database must conform to a prespecified, user- or application-definedDTD. In this paper, we propose a novel formalism, XML Integration Grammars (XIGs), for specifying DTD-directed integration of XML data. Abstractly, an XIG maps data from multiple XML sources to a target XML document that conforms to a predefined DTD. An XIG extracts source XML data via queries expressed in a fragment of XQuery, and controls target document generation with tree-valued attributes and the target DTD. The novelty of XIGs consists in not only their automatic support for DTD-conformance but also in their composability: an XIG may embed local and remote XIGs in its definition, and invoke these XIGs during its evaluation. This yields an important modularity property for our XIGs that allows one to divide a complex integration task into manageable sub-tasks and conquer each of them separately. To efficiently evaluate XIGs we provide algorithms for merging XML queries in an XIG and for scheduling queries and embedded XIGs. These lead to an effective framework, as well as a design tool for XQuery, for effectively specifying and computing complex, DTD-directed XML integration.

Seamless roaming in heterogeneous circuit-switched and IP networks is key for successful migration towards all-IP. The Unified Mobility Manager (UMM) keeps track of users' locations whatever network they are roaming, and that routes calls from any originating to any terminating network. In a UMM enabled network, calls are addressed to users and dynamically routed to the active devices. This notion of user mobility breaks up the traditional coupling of addresses and devices, and replaces it with rules for the dynamic mapping from logical to device addresses. We present the UMM multi-protocol subscriber base that is built upon the concept of Incoming Call Handling (ICH) profiles that are the addressable entities in the UMM. An ICH profile contains all address level service data as well as the routing rules to the actual terminating devices. We also present our implementation of the UMM subscriber base in detail, and compare the UMM approach with two other approaches - a federated approach and an approach that combines ANSI-41 user mobility with GAIT interworking.

In this paper, we study the problem of temporal consistency maintenance where a certain degree of temporal inconsistency is tolerable. We propose a suite of statistical more-less (SML) approaches to tradeoff of quality of service (QoS) of temporal consistency against the number of supported transactions. We begin with a base-line algorithm, SML-BA, which provides the requested QoS of temporal consistency. We then propose SML with optimization (SML-OPT) to further improve the QoS by better utilizing the excessive CPU capacity. Finally, we enhance SML-OPT with a slack reclaiming scheme (SML-SR). The reclaimed slacks are used to process jobs whose required computation time is larger than the guaranteed computation time. Simulation experiments are conducted to compare the performance of these schemes (SML-BA, SML-OPT and SML-SR) together with the deterministic more-less and half-half schemes. Our results show that the SML schemes are effective in trading off the schedulability of transactions and the QoS guaranteed. Moreover, SML-SR performs best and offers a significant QoS improvement over SML-BA and SML-OPT.

Typically, temporal validity of real-time data is maintained by periodic update transactions. We examine the problem of period and deadline assignment for these update transactions such that 1) these transactions can be guaranteed to complete by their deadlines and 2) the imposed CPU workload is minimized. To this end, we propose a novel approach, named the More-Less approach. By applying this approach, updates occur with a period which is more than the period obtained through traditional approaches, but with a deadline which is less than the traditional period. We show that the More-Less approach is better than existing approaches in terms of schedulability and the imposed load. We examine the issue of determining the assignment order in which transactions must be considered for period and deadline assignment so that the resulting CPU workloads can be minimized. To this end, the More-Less approach is first examined in a restricted case where the shortest validity first (SVF) order is shown to be an optimal solution. We then relax some of the restrictions and show that SVF is an approximate solution which results in CPU workloads that are close to the optimal solution. Our analysis and experiments show that the More-Less approach is an effective design approach that can provide better schedulability and reduce update transaction CPU workload while guaranteeing data validity constraints.

In this paper, issues involved in the design of a real-time database which maintains data temporal consistency are discussed. The concept of data-deadline is introduced and time cognizant transaction scheduling policies are proposed. Informally, data-deadline is a deadline assigned to a transaction due to the temporal constraints of the data accessed by the transaction. Further, two time cognizant forced wait policies which improve performance significantly by forcing a transaction to delay further execution until a new version of sensor data becomes available are proposed. A way to exploit temporal data similarity to improve performance is also proposed. Finally, these policies are evaluated through detailed simulation experiments. The simulation results show that taking advantage of temporal data semantics in transaction scheduling can significantly improve the performance of user transactions in realtime database systems. In particular, it is demonstrated that under the forced wait policy, the performance can be improved significantly. Further improvements result by exploiting data similarity.

Typically, temporal validity of real-time data is maintained by periodic update transactions. In this paper we examine the problem of period and deadline assignment for these update transactions such that (1) these transactions can be guaranteed to complete by their deadlines and (2) the imposed workload is minimized. To this end, we propose a novel approach, named More-Less principle. By applying this principle, updates occur with a period which is more than the period obtained through traditional approaches but with a deadline which is less than the traditional period. We show that the More-Less principle is better than existing approaches in terms of schedulability and the imposed load. We examine the issue of determining the assignment order in which transactions must be considered for period and deadline assignment so that the resulting workloads can be minimized. To this end, the More-Less principle is first examined in a restricted case where the Shortest Validity First (SVF) order is shown to be an optimal solution. We then relax some of the restrictions and show that SVF is an approximate solution which results in workloads that are close to the optimal solution. Our analysis and experiments show that the More-Less principle is an effective design principle that can provide better schedulability and reduce update transaction workload while guaranteeing data validity constraints.

In this paper, issues involved in the design of a real-time database which maintains data temporal consistency are discussed. The concept of data-deadline is introduced and time cognizant non-waiting transaction scheduling policies are proposed. Further, two time cognizant forced wait policies which improve performance significantly by forcing a transaction to delay further execution until a new version of sensor data becomes available are proposed. A way to exploit temporal data similarity to improve performance is also proposed. Finally, these policies are evaluated through detailed simulation experiments. The simulation results show that taking advantage of temporal data semantics in transaction scheduling can significantly improve the performance of user transactions in real-time database systems. In particular, it is demonstrated that under the forced wait policy, the performance can be improved significantly. Further improvements result by exploiting data similarity.