Preserve Data Consistency Research Articles

TOWARDS THE INTEGRATED ADOPTION OF BLM AND DT IN AECO-FM SECTOR: A SCIENTOMETRIC APPROACH

The Architecture, Engineering, Construction, Operation and Facility Management (AECO-FM) industry presents a well-known discrepancy in terms of productivity and digitalization compared with other sectors. The introduction of building lifecycle management (BLM) could lead to several benefits covering all the phases of the building lifecycle (BLC) and enabling the implementation of a model-based integrated management system. Nevertheless, its adoption is far from being spread across the construction field and the case studies in the existing literature present several criticalities related to the adopted technologies and policies which hinder the complete development of a BLM system. To overcome these obstacles, digital twins (DTs) could be exploited to enhance the collaboration among actors, preserving the integrity of data, and offering a multidisciplinary environment to carry out realistic simulations. The purpose of this research is to conduct a scientometric analysis to evaluate the relationships between the topics of BLM and DT in the AECO-FM sector. The most common key studies and patterns are identified by processing bibliometric metadata. The most investigated topics related to BLM and DT are system design, system management, and Building Information Modeling (BIM) applications. The analysis reveals a gap regarding the connection between BLM and DT, identifying a potential research area that is currently unexplored in the academic field. The integrated adoption of these topics could contribute to promoting a holistic approach in the built environment by preserving data consistency, reducing information loss between design and construction phases, and strengthening connectivity among the actors involved in the process.

Composite Object Normal Forms: Parameterizing Boyce-Codd Normal Form by the Number of Minimal Keys

We parameterize schemata in Boyce-Codd Normal Form (BCNF) by the number n of minimal keys they exhibit. We show that n quantifies a trade-off between access variety and update complexity. Indeed, access variety refers to the number of different ways by which every entity over the schema is represented uniquely, while update complexity refers to the number of attribute sets for which uniqueness needs to be preserved during updates. As normalization aims at minimizing the level of effort required to preserve data consistency during updates, we establish an algorithm that returns a lossless, dependency-preserving 3NF decomposition where the subset of output schemata not in BCNF is minimized and redundant BCNF schemata are eliminated from the highest to the lowest n exhibited. In particular, if a lossless, dependency-preserving BCNF decomposition exists, our algorithm returns one where the maximum n across all output schemata is minimized. Experiments with synthetic and real-world data quantify the impact of n on the update and query performance over schemata in BCNF with n minimal keys, and show insight into the efficacy of our algorithm suite.

Cardinality constraints and functional dependencies in SQL: Taming data redundancy in logical database design

We investigate the combined class of cardinality constraints and functional dependencies over SQL tables. As our first contribution, we establish a finite ground axiomatization and quadratic-time algorithm for deciding their implication problem. As our second contribution, we characterize when finite Armstrong tables exist for this class, and show how to compute finite representations of Armstrong tables for every given input. While there are extreme cases where the size of an Armstrong representation is logarithmic or necessarily exponential, our extensive experiments suggest that the size is low-degree polynomial on average. As our third contribution, we propose a new family of syntactic normal forms for the logical design of SQL tables, of which the well-known Boyce–Codd Normal Form is a special case. Our normal form characterizes SQL schemata that have an a priori upper bound on the number of records in which any redundant data value may occur in any database instance over the schema. Such bounds tame the number of records requiring updates to preserve data consistency.

Deep Ensemble-Based Approach Using Randomized Low-Rank Approximation for Sustainable Groundwater Level Prediction

Groundwater is the most abundant freshwater resource. Agriculture, industrialization, and domestic water supplies rely on it. The depletion of groundwater leads to drought. Topographic elevation, aquifer properties, and geomorphology influence groundwater quality. As the groundwater level data (GWL) are time series in nature, it is challenging to determine appropriate metrics and to evaluate groundwater levels accurately with less information loss. An effort has been made to forecast groundwater levels in India by developing a deep ensemble learning approach using a double-edge bi-directed long-short-term-memory (DEBi-LSTM) model approximated with a randomized low-ranked approximation algorithm (RLRA) and the variance inflation factor (VIF) to reduce information loss and to preserve data consistency. With minimal computation time, the model outperformed existing state-of-the-art models with 96.1% accuracy. To ensure sustainable groundwater development, the proposed work is discussed in terms of its managerial implications. By applying the model, we can identify safe, critical, and semi-critical groundwater levels in Indian states so that strategic plans can be developed.

BVAGQ-AR for Fragmented Database Replication Management

Large amounts of data have been produced at a rapid rate since the invention of computers. This condition is the key motivation for up-to-date and forthcoming research frontiers. Replication is one of the mechanisms for managing data, since it improves data accessibility and reliability in the distributed database environment. In recent years, the amount of various data grows rapidly with widely available low-cost technology. Although we have been packed with data, we still have lacked of knowledge. Nevertheless, if the impractical data is used in database replication, this will cause waste of data storage and the time taken for a replication process will be delayed. This paper proposes Binary Vote Assignment on Grid Quorum with Association Rule (BVAGQ-AR) algorithm in order to handle fragmented database synchronous replication. BVAGQ-AR algorithm is capable for partitioning the database into disjoint fragments. Fragmentation in distributed database is very useful in terms of usage, reliability and efficiency. Managing fragmented database replication becomes a concern for the administrator because the distributed database is disseminated into split replica partitions. The result from the experiment shows that handling fragmented database synchronous replication through proposed BVAGQ-AR algorithm able to preserve data consistency in distributed environment.

Cost Saving Replication Technique in Data Grid

Replication is a useful technique for distributed database systems. An existing popular technique that has been used before are Read One-write-All Monitoring Synchronization Transactions System (ROWA-MSTS) and Hierarchical Replication Scheme (HRS). Nevertheless, these techniques have their flaws in terms of replication time taken. Subsequently, ROWA and HRS take more executing time in order to complete a transaction. It is because they have to copy their data to all sites. This research, Binary Voting Fragmented Database Replication (BVFDR) model, the some-data-to-some-sites technique is proposed. In order to handle transactions in the systems, BVFDR considers the neighbouring servers binary vote assignment to its logical grid structure on fragmented data copies. Therefore, it reduces storage capacity. An experiment has been carried out in three replicated servers. The results have been compared with existing techniques such ROWA and HRS. The result shows that BVFDR is able to preserve data consistency and outperformed in terms of time taken for a complete transaction compare to existing techniques. Overall, BVFDR able to handle fragmented data replication as well as capable to manage transaction management in distributed database environment by maintaining consistency of the data through the synchronization approach.

Deep null space learning for inverse problems: convergence analysis and rates

Recently, deep learning based methods appeared as a new paradigm for solving inverse problems. These methods empirically show excellent performance but lack of theoretical justification; in particular, no results on the regularization properties are available. In particular, this is the case for two-step deep learning approaches, where a classical reconstruction method is applied to the data in a first step and a trained deep neural network is applied to improve results in a second step. In this paper, we close the gap between practice and theory for a particular network structure in a two-step approach. For that purpose, we propose using so-called null space networks and introduce the concept of -regularization. Combined with a standard regularization method as reconstruction layer, the proposed deep null space learning approach is shown to be a -regularization method; convergence rates are also derived. The proposed null space network structure naturally preserves data consistency which is considered as key property of neural networks for solving inverse problems.

Managing Database Replication Using Binary Vote Assignment on Grid Quorum with Association Rule

Nowadays, many organizations deploy the database application systems in order to manage their business operation. Administrations is required to deliver up to date data to users who live distantly, thus replicated databases can be one solution to increase the business operations performance in the distributed environment. Even though data availability is increased, common existing replication strategies neglect the correlation among the different data files in a Distributed Database Systems. In this paper, Binary Vote Assignment on Grid Quorum with Association Rules (BVAGQ-AR) has been proposed to manage the database replication. This technique combines data replication and data mining approaches in order to decrease the job processing time. The result shows that BVAGQ-AR has the lowest job processing time compared to other techniques. BVAGQ-AR performs 66.548 ms to complete a transaction compared to BRS with 137.157 ms, HRS with 257.928 ms, and ROWA with 262.243 ms. From the results, it is proved that managing replication and transaction through the proposed BVAGQ-AR able to preserve data consistency with low job processing time.

Managing Fragmented Database Using BVAGQ-AR Replication Model

One of the mechanisms for managing data is replication since it improves data access and reliability. However, the amount of various data grows rapidly since technology is widely available at a low-cost. Problem arises when the database is packed with data, but it has lacked of knowledge. If the unreasonable data is used in database replication, it will cause waste of data storage and delay the time taken for a replication process. This paper proposes a new algorithm namely Binary Vote Assignment on Grid Quorum with Association Rule (BVAGQ-AR) to handle fragmented database replication. BVAGQ-AR algorithm is capable of partitioning the database into disjoint fragments. Handling fragmented database replication becomes challenging issue to administrator since the distributed database is disseminated into split partitions or fragments. This paper will discuss about how to build reliable system by using the proposed BVAGQ-AR algorithm for distributed database fragmentation. The result shows that managing fragmented database replication through proposed BVAGQ-AR algorithm able to preserve data consistency.

Managing Fragmented Database Replication for Mygrants Using Binary Vote Assignment on Cloud Quorum

Replication in distributed database is the process of copying and maintaining database objects in multiple databases that make up a distributed database system. In this paper, we will manage fragmented database replication and transaction management for Malaysian Greater Research Network (MyGRANTS) using a new proposed algorithm called Binary Vote Assignment on Cloud Quorum (BVACQ). This technique will combine replication and fragmentation. Fragmentation in distributed database is very useful in terms of usage, efficiency, parallelism and also for security. This strategy will partition the database into disjoint fragments. The result shows that managing replication and transaction through proposed BVACQ able to preserve data consistency. It also increases the degrees of parallelism. This is because by using fragmentation, replication and transaction can be divided into several subqueries that operate on the fragments.

A Primary Shift Protocol for Improving Availability in Replication Systems

Backup Replication (PBR) is the most common technique to achieve availability in distributed systems. However, primary failure remains a crucial problem that threatens availability. When the primary fails, backup nodes in the system have to elect a new primary node in order to maintain adequate system's operation. During election, the system suffers from transaction loss, communication overhead due to messages exchange necessary to preserve data consistency, and a notable delay caused by the execution of Leader Election Algorithms (LEA). Primary failures can be unpredictable (i.e., unplanned), such as primary node crashes and network outages, or predictable (i.e., planned), such as primary's scheduled shutdown to perform routine maintenance or software upgrade. Traditionally, PBR employ LEA to recover from both unplanned and planned outages. In this paper, we propose a novel protocol, called Primary Shift Replication (PSR), to avoid election during planned outages. PSR shifts the primary role from the current primary to another scheduled node (without election) when a planned outage is about to occur. Number of messages and communication time required to shift the primary node to another node is much less than number of messages and time required to perform leader election; therefore, PSR improves system's availability. Moreover, PSR guarantees no transactions loss during the shift mode, hence, it preserves data consistency.

Managing transactions on grid-neighbour replication in distributed systems

Managing transactions is very important in distributed databases in order to preserve data consistency and reliability of the systems. This paper presents a new design to manage transactions on neighbour replication in a distributed database system. We address how to build a reliable system for managing transactions on a neighbour replication grid (NRG) in terms to preserve the data consistency and support fault-tolerance. We first recall the model and technique of NRG that impose neighbours binary vote assignment to its logical grid structure on data copies. We extend our work in managing transactions to normal and failure cases. Finally, the implementation of the system is presented.

Discovering data quality rules

Dirty data is a serious problem for businesses leading to incorrect decision making, inefficient daily operations, and ultimately wasting both time and money. Dirty data often arises when domain constraints and business rules, meant to preserve data consistency and accuracy, are enforced incompletely or not at all in application code. In this work, we propose a new data-driven tool that can be used within an organization's data quality management process to suggest possible rules, and to identify conformant and non-conformant records. Data quality rules are known to be contextual, so we focus on the discovery of context-dependent rules. Specifically, we search for conditional functional dependencies (CFDs), that is, functional dependencies that hold only over a portion of the data. The output of our tool is a set of functional dependencies together with the context in which they hold (for example, a rule that states for CS graduate courses, the course number and term functionally determines the room and instructor). Since the input to our tool will likely be a dirty database, we also search for CFDs that almost hold. We return these rules together with the non-conformant records (as these are potentially dirty records). We present effective algorithms for discovering CFDs and dirty values in a data instance. Our discovery algorithm searches for minimal CFDs among the data values and prunes redundant candidates. No universal objective measures of data quality or data quality rules are known. Hence, to avoid returning an unnecessarily large number of CFDs and only those that are most interesting, we evaluate a set of interest metrics and present comparative results using real datasets. We also present an experimental study showing the scalability of our techniques.

Preserving Data Consistency through Neighbor Replication on Grid Daemon

In modern distributed systems, replication receives particular attention for providing high data availability, fault tolerance and enhance the performance of the system. It is an important mechanism because it enables organizations to provide users with access to current data where and when they need it. However, this way of data organization introduces low data consistency and data coherency as more than one replicated copies need to be updated. Expensive synchronization mechanisms are needed to maintain the consistency and integrity of data among replicas when changes are made by the transactions. In this paper, we present Neighbor Replication on Grid (NRG) daemon in order to manage replication and transactions in distributed system. NRG Transaction Model has been implemented in order to preserve the data consistency and availability. Based on experiment and result, it shows that NRG daemon guarantees consistency and obey serializability through the synchronization approach. Key word: Distributed system, replication, consistency, synchronous, transaction, serializability.

Concurrent view maintenance scheme for soft real-time data warehouse systems

The data warehouse should be refreshed periodically or aperiodically in order to prevent OLAP transactions from reading extremely stale data. This refreshing process is referred to as view maintenance. There have been many researches on ways to preserve data consistency during the process of view maintenance. The simplest approach among them is to perform view maintenance in the nighttime while OLAP transactions are allowed to be active only in daytime. However, this approach cannot be applied to globalized corporations because no common idle time is available for such corporations. In this paper, we propose a new algorithm for performing view maintenance without blocking OLAP transactions. Our algorithm improves data freshness by allowing one or more maintenance transactions and OLAP transactions to be executed concurrently. We introduce the details of algorithm with some examples, present proofs of devised scheme, and then evaluate performance gains by means of simulation method.

Atomic Writes for data integrity and consistency in shared storage devices for clusters

With the recent trend to use storage area networks in distributed and cluster systems there is a need to improve the integrity and consistency guarantees of stored data in the presence of node or network failures. Currently, the main method for preserving data integrity and consistency is by logging techniques, e.g. journaling. This paper presents a new general method for preserving data consistency by Atomic multi-block Writes. The Atomic Writes method guarantees that either all the blocks in a write operation are written or no blocks are written at all. Its main advantage is that it does not require a recovery phase after a failure. The Atomic Writes method should be implemented in both the operating system and the storage system levels. It is easy to use and to implement. We present the method, its implementation and an example of its use for handling meta-data consistency in an existing file system that supports shared storage.

External memory management and simplification of huge meshes

Very large triangle meshes, i.e., meshes composed of millions of faces, are becoming common in many applications. Obviously, processing, rendering, transmission, and archiving of these meshes are not simple tasks. Mesh simplification and LOD management are a rather mature technology that, in many cases, can efficiently manage complex data. But, only a few available systems can manage meshes characterized by a huge size: RAM size is often a severe bottleneck. In this paper, we present a data structure called Octree-based External Memory Mesh (OEMM). It supports external memory management of complex meshes, loading dynamically in main memory only the selected sections and preserving data consistency during local updates. The functionalities implemented on this data structure (simplification, detail preservation, mesh editing, visualization, and inspection) can be applied to huge triangles meshes on low-cost PC platforms. The time overhead due to the external memory management is affordable. Results of the test of our system on complex meshes are presented.

Abort-oriented concurrency control for real-time databases

There has been growing interest in the performance of transaction systems that have significant response time requirements. These requirements are usually specified as hard or soft deadlines on individual transactions and a concurrency control algorithm must attempt to meet the deadlines as well as preserve data consistency. This paper proposes a class of simple and efficient abort-oriented concurrency control algorithms in which the schedulability of a transaction system is improved by aborting transactions that introduce excessive blockings. We consider different levels of the aborting relationship among transactions and evaluate the impacts of the aborting relationship when the relationship is built in an online or offline fashion. We measure aborting overheads on a system running the LynxOS real time operating system. The strengths of the work are demonstrated by improving the worst-case schedulability of an avionics example, a satellite control system, and randomly generated transaction sets.

Performance of a two-phase commit protocol

Distributed database systems are subject to data inconsistency in the presence of failures. In order to coordinate the execution of the subtransactions of a global transaction, it is necessary to commit or abort all these subtransactions to maintain data consistency. Two classes of atomic commitment protocols have been proposed in the literature. The first class, known as fault tolerant protocol, aborts a transaction without checking for alternative solutions. The second, optimistic protocol, ignores the occurrence of errors and assumes that a global transaction commits in most cases. However, this would lead to compensating transactions so as to preserve data consistency, in case the global transaction aborts. In this paper, we study the performance of a Prudent Two-Phase Commit protocol, which in the presence of failures does not abort a transaction needlessly. The prudent protocol gives the transaction a second chance before deciding to abort. This prudent approach prevents a transaction from aborting in case of transient communication error or failure. In this way, it improves system performance and reliability. For the purpose of this study, we simulate a distributed database system. The performance of the simulated distributed system is measured while using the Prudent, Basic, and Optimistic Two-Phase Commit protocols. The performances of such protocols are compared and evaluated according to a number of widely accepted performance metrics. The final performance results are presented with a discussion and interpretation of the graphs generated by the simulation. The results of these simulations confirm the improvement in system performance with the Prudent Two-Phase Commit protocol.

Chronological scheduling of transactions with temporal dependencies

Database applications often impose temporal dependencies between transactions that must be satisfied to preserve data consistency. The extant correctness criteria used to schedule the execution of concurrent transactions are either time independent or use strict, difficult to satisfy real-time constraints. On one end of the spectrum, serializability completely ignores time. On the other end, deadline scheduling approaches consider the outcome of each transaction execution correct only if the transaction meets its real-time deadline. In this article, we explore new correctness criteria and scheduling methods that capture temporal transaction dependencies and belong to the broad area between these two extreme approaches. We introduce the concepts of succession dependency and chronological dependency and define correctness criteria under which temporal dependencies between transactions are preserved even if the dependent transactions execute concurrently. We also propose a chronological scheduler that can guarantee that transaction executions satisfy their chronological constraints. The advantages of chronological scheduling over traditional scheduling methods, as well as the main issues in the implementation and performance of the proposed scheduler, are discussed.