Abstract: The timing constraint is expressed in the form of a deadline, a certain time in the future by which a transaction needs to be completed. In real-timedatabase systems, the correctness of transaction processing depends not only on maintaining consistency constraints and producing correct results but also on the time at which a transaction is completed. Transactions must be scheduled in such a way that they can be completed before their corresponding deadlines expire. The gap between the performance of processors and the performance of disks is enlarged and the performance bottleneck in a computer system is shifted to the storage subsystem. When multimedia applications become more important, the gap problem becomes more serious. This motivates the need for more efficient use of the transaction scheduler in order to maximize disk throughput and minimize seek time. Earliest-deadline-first (EDF), is good for scheduling real-time tasks in order to meet timing constraint. However, it is not good enough for scheduling real-time disk tasks to achieve high disk throughput. In contrast, although SCAN can maximize disk throughput, its schedule results may violate real-time requirements. Thus, during the past few years, various approaches were proposed to combine EDF and SCAN (e.g., SCAN-EDF and RG-SCAN) to resolve the real-time disk-scheduling problem. However, in previous schemes, real-time tasks can only be rescheduled by SCAN within a local group. Therefore, to improve the performance a new algorithm is proposed based on globally seek optimizing scheduling approach: GSR (Globally Seek-optimizing Rescheduling) algorithm. TGGSR achieves higher disk throughput as compare to EDF and GSR. This approach first generates the initial schedule considering the track locations of the transactions and then this input schedule is decomposed into the SCAN groups and final rescheduled result is found.
Data available in realtimedatabase are highly valuable. Data stored in databases keep growing as a result of business requirement and user need. Transmission of such a large quantity of data requires more money. Managing such a huge amount of data in realtime environment is a big challenge. Traditionally available methods faces more difficultly in dealing with such a large databases. In this Research Paper we provide an overview of various data compression techniques and a solution to how to optimize and enhance the process to compress the realtime databases and achieve better performance than conventional database system. This research paper will provide a solution to compress the realtime data more effectively and reduce the storage requirement, cost and increase speed of backup. The compression of data for realtime environment is developed with Iterative length Compression Algorithm. This Algorithm provide parallel storage backup for realtimedatabase system .
Abstract— A priority table base CPU scheduling algorithm in real-timedatabase (RTDBS) is presented. It is applicable in environments where data requests arrive with different requirements such as real-time deadline, and user priority. Previous work on CPU scheduling is based on traditional CPU scheduler for meeting the real-time deadlines. The general idea is based on modeling the CPU scheduler in RTDBS requests as points in the multi-dimensional space, where each of the dimensions represents one of the parameters i.e. one dimension represents the deadline of task , another represents the criticalness or value and a third dimension represents the priority of the request, etc. The CPU scheduling problem reduces the problem of finding a linear order to scheduling these multidimensional points. Priority table base algorithms are adopted to define a linear order for sorting and scheduling objects that lie in the multi-dimensional space. This generalizes the one dimensional CPU scheduling algorithms (e.g., EDF, MCF, and CDF). Several techniques are presented to show how a CPU scheduler deals with the progressive arrival of task over time. In this dissertation traditional CPU scheduling algorithms are investigated, implemented and compared with priority table base CPU scheduling algorithm in realtimedatabase system.
A real-timedatabase is a processing system designed to handle workloads whose state is constantly changing. This differs from traditional databases containing persistent data, mostly unaffected by time. Real-time processing means that a transaction is processed fast enough for the result to come back and be acted on right away. Real-time databases are useful for accounting, banking, law, medical records, multi-media, process control, reservation systems, and scientific data analysis.
coordinate the activities on the factory floor. Timely monitoring of the environment as well as timely processing of the sensed information is necessary. In addition to the timing constraints that arise from the need to continuously track the environment, timing correctness requirements in a real-timedatabase system also arise because of the need to make data available to the controlling system for its decision making activities. Besides robotics, applications such as medical patient monitoring, programmed stock trading, commerce, electronic banking, telecommunications and military command and control systems like submarine contact tracking require timely actions as well as the ability to access and store complex data that reflects the state of the application’s environment. That is, data in these applications must be valid, or fresh, when it is accessed in order that the application performs correctly. A RTDB is composed of real-time objects which are updated by periodic sensor transactions. An object in the database models in a real world entity, for example, the position of an aircraft. A real- time object is one whose state may become invalid with the passage of time. Associated with the state is a temporal validity interval. To monitor the states of objects faithfully, a real-time object must be refreshed by a sensor transaction before it becomes invalid, that is, before its temporal validity interval expires. The actual length of the temporal validity inter val of a real-time object is application dependent.
Abstract: The design and implementation of real-timedatabase presents many new challenging problems. Compared with conventional database, real-timedatabase have distinct features: they must maintain the coherent data while satisfy the timing constraints associated with transaction. With evolution of Earliest Deadline First (EDF) in 1973 by LIU and LAYLAND, laid the path for development of RTDB, it is very inefficient in overloaded workload conditions. Adaptive Earliest Deadline (AED) improves the performance which uses feedback control mechanism to detect overloaded condition and tries to attain HIT ratio 1.0. There prevails the risk of losing transaction with extremely high value may cause severe losses to system. A extension of AED called Hierarchical Earliest Deadline (HED) provide solution by establishing the value based bucket hierarchy thus ensuring the completion of high value transaction, in which value assigned reflects the return expected to receive if the transaction commits before its deadline. A new multi-dynamic priority real-time scheduling algorithm named MDTS is studied, it considers various characteristic parameters of transactions, and hard and soft real- time transactions are treated differently. In order to know the time required by the transactions and how to minimize it, it is mandatory to study the different parameters required for realtime disk scheduling. This task can be achieved with the help of a mathematical model which shows how scheduling result of any algorithm can be evaluated. This paper derives a new scheduling algorithm that combines MDTS scheduling algorithm with G-EDF. Then scheduling results of MDTS and our proposed algorithm are evaluated and compared.
We conducted an broad set of simulation experiments using the above mentioned parameters in Table 1 & 2 using simulation languages GPSS . The simulation can use different simulation languages such as C++SIM, DeNet [10,9] etc. Commit percentage and Abort percentage were used as measures for the performance metrics in our simulation results. Commit percentage is the percentage of input transactions that the system is able to complete before their deadline and Abort percentage is the percentage of input transactions that the system is unable to complete before their deadline. We conducted simulation under normal and heavy loads with different settings of workload parameters like as Numsites, DBsize(File size), DistDegree (File selection time), and with other corresponding parameter values. We considered 8 distributed sites, with 8 files and other parameter values were kept constant. The one phase EP protocol significantly reduces the overhead of commit processing by eliminating an entire phase, making it especially attractive in the realtimedatabase system in distributed environment.
Many applications exist in which hard real-time transactions must be supported; examples include embedded control applications, defense systems, and avionics systems. With the recent advent of workstation-class multiprocessor systems, multiprocessor-based implementations of these systems are of growing importance. Un- fortunately, the problem of implementing hard real-time transactions on multiprocessors has received relatively little attention. With most (if not all) previously-proposed schemes for implementing such transactions, the kind of transactions that can be supported is often limited, and worst-case performance is often poor, adversely impacting schedulability. Transactions have many attributes (priority, execution time, resource usage, etc.), the most important of which is the deadline. A transaction’s deadline is the mean by which the system designer can specify its timeliness requirement. A way of classifying transactions is based on the effects of their deadlines not being met. Hard real-time transactions are those which can have catastrophic consequences if they are not executed on time. Soft real-time transactions, on the other hand, will only degrade the system’s performance when not completed within their deadline. A special type of soft real- time transaction is a firm real- time transaction, which has no value to the system when its deadline is not met and should therefore be aborted. A pragmatic way to design real-time databases is to minimize the ratio of transactions that miss their deadlines. This strategy is, however, a step short of meeting hard real-time requirements, under which missing a single deadline can lead to catastrophic effects. Most commercial databases that claim real-time properties are not suitable for hard real-time applications. Real-timedatabase designers are faced with a vast
Abstract— Recent advances in wireless communication networks and portable computers have led to the emergence of a new research area called mobile computing systems. An important part of the research conducted in mobile computing systems has been done on mobile data management. What make the mobile data management different from the conventional data management are the mobility of the users or the computers connected to the system, and the resource constraints such as wireless bandwidth and battery life. As a result of such distinctive features of mobile systems, the data management techniques developed for conventional distributed database systems may not work well in a mobile environment. Research contributions are required in a variety of areas, such as distribution of data on mobile and/or non-mobile computers, processing of queries and transactions submitted by mobile users, maintaining the consistency of data cached on mobile computers, and so on. Another important issue that needs to be considered in mobile data management is the requirement of processing queries and transactions within certain time limits in order to maintain the temporal validity of the data accessed by those queries and transactions. Our basic objective in this project is a thorough investigation of the issues to develop various types of methods for mobile data management in response to the requirements mentioned.
weaknesses can easily be corrected by amending and re-running the scripts. Collecting new data in light of identified problems becomes trivial, as does updating the dataset. The advantage of using computer scripts to code data is perhaps greatest when the repetitive element of the task at hand is high, and the coding challenge trivial. This is also where we would expect human coders to make most mistakes due to boredom and for scripts to produce most accurate results. The coding of MEPs‟ background information is one such task. We have written computer scripts for the collection and maintenance of a complete database of all directly elected members of the European Parliament since 1979. The database is available at http://folk.uio.no/bjornkho/MEP. The database is password protected. Guidelines for use of
Abstract: Real-time disk scheduling (RTDS) plays an important role in time-critical applications. Due to rigorous timing requirements for error free output, data must be accessed under real-time constraints. Therefore how to maximize data throughput under real-time constraints poses a big challenge in the design of real-time disk scheduling algorithms. Numbers of algorithms are proposed to schedule realtime transactions in order to increase the overall performance. Currently Earliest-Deadline-First (EDF) is a basic algorithm which meets the realtime constraints, but it gives poor disk throughput. While SCAN gives maximum disk throughput but violates the realtime constraints. Thus, various algorithms proposed later combined EDF and SCAN to improve the real-time performance. In previously proposed algorithms (e.g. SCAN-EDF, DM-SCAN & RG-SCAN) the transactions are scheduled by forming groups, based on certain conditions. But these approaches are locally seek optimizing Schemes i.e. transactions can only be rescheduled by SCAN within a local group. Once a transaction belongs to a certain group, it cannot be rescheduled to a different group, even though such a rescheduling gives better performance. Therefore, to improve the performance a new algorithm is proposed based on globally seek optimizing scheduling approach: GSR (Globally Seek-optimizing Rescheduling) algorithm. In this algorithm, relation between SCAN and EDF is established by using EDF-to-SCAN mapping (ESM). Then on the basis of ESM groups are formed and transactions are rescheduled globally within that groups using GSR algorithm. Different from previous approaches, a transaction in GSR may be rescheduled to anywhere in the input schedule to optimize data throughput. Owing to such a globally rescheduling characteristic, GSR obtains a higher disk throughput than previous approaches.
A common agenda to model a distributed replicated real-timedatabase has been designed for small and medium scale distributed real-timedatabase systems. Both data and transactions timing constrains are maintained. The presented framework has been extended to model large scale database systems. This has been done by presenting a general outline that considers improving the scalability by using an existing static segmentation algorithm applied on the whole database on node allocation. It is desirable to implement this framework on a distributed real-timedatabase system and evaluate it with real transactions requirements. We are currently using a replication control algorithm. Framework to model real-time replicated database will be used as a framework for our real-timedatabase, which is not fully supported for distributed database. We are also looking into implementing the segmentation part as a contribution in solving the scalability problem of distributed real-timedatabase systems. The design issues raise in this paper is the challenges for new researchers. We are reviewing the issues and try to develop some protocols for distributed realtime databases which enhance the performances of replicated database.
Tube Hydroforming (THF) which is a subset of the Hydroforming process is a metal forming process in which a tube is plastically expanded into a die cavity by the simultaneous action of fluid pressure as well as axial material feed given to the expansion zone, such that the tube takes the shape of the die cavity. The process is relatively new and still has a lot of research potential. THF is a path dependant process and as a result it is very important to get the correct loading path for the required forming of the tube. A loading path is basically a plot of the internal pressure vs. the feed that is required for the forming. Depending upon these two variables, the loading path differs for different forming conditions. There is a need for a systematic knowledge base for getting all these loading paths to increase the utility of Tube Hydroforming as well as to fuel further research in the area. This research is focused on the increase in utility of the THF process by development of a real-timedatabase which would be able to predict the loading path for a particular set of forming conditions and variables and display them to the user. The research will include a complete step wise development of the database from doing a survey on the current commercially available THF components, their classification into families, development of the theory behind the prediction of loading paths, simulations carried out to the actual database operation. The study aims at putting to practice a real-timedatabase, a first of its kind which could be directly coupled with some modifications, to the THF presses in the industry.
In this paper a realtime data transmission and database management system is implemented using wireless technology .Unlike the older systems temperature measurements are transmitted automatically and realtimedatabase is updated periodically using Wi-Fi technology. An automated data acquisition report is generated for day, week and month. Here we discuss how the data is transmitted automatically from one location of industry to server and the database is maintained for each data at equal time intervals. This method doesn’t require any human intervention to carry measured data from one location to another location; once data is measured it will be sent from system to database server using Wi-Fi technology. The future work of this project involve building an Android application that will show a report generated by the microprocessor .This report can be checked in real-time using the app and it is more convenient and efficient than the conventional method.
Real-time face recognition nowadays is a necessary feature in advanced surveillance systems. It is a program that guarantees a response within strict time constraints. Real-time response times are understood to be in the order of milliseconds and sometimes microseconds. Thus, in this project the response time between the captured image and the data based result is expected to be in real-time.
Santhosh Kumar GajendranThis document discussed about the motivation, evolution and some implementations of the NoSQL databases. The NoSQL databases were broadly classified into 3 categories and we analyzed few data store implementations that fall into those categories. Each of them has been motivated by varying requirements which has led to their development mostly from the industry. Each data store and its implementation has strengths at addressing specific enterprise or cloud concerns such as being easy to operate, providing a flexible data model, high availability, high scalability and fault tolerance. Each NoSQL database should be used in a way that it meets its claims and the overall system requirements. It was seen as to how the different data stores were designed to achieve high availability and scalability at the expense of strong consistency. The different data stores use different techniques to achieve this goal and seem to suit well for their requirements. The following table gives a summary of some of the features across the data stores that have been discussed here.[8-10]
Abstract. 3D visualization is important content of digital terrain information space performance. Based on high-precision bathymetric data by NOAA and high-resolution satellite image data, this paper construct the seafloor at the Mariana Trench and the lake Superior scene data model, which roaming anywhere at any angle of view in the 3D terrain scene are interactively obtained and dynamically presented the nature looking in real-time truly and intuitively using Vega-Prime system. As the benefit with using 3-D visualization technology, we can go to visit places where we have never been to.
present in the ECG signal . Typical examples of these disturbs are baseline noises, power line interference, electrode contact noise, and motion artifacts. The presence of these noises makes the automatic detection of QRS complexes very difficult and it is necessary an onerous pre- processing stage to treat the signal. Therefore, in order to provide real-time ECG analysis, most of these algorithms must be implemented on high-performance computing units, such as a personal computers, very powerful micro- controllers or DSP. Today, the need to monitor heart conditions outside the hospital for several days has increased the use of processing systems characterized by low costs and small size, such as wearable devices . On these wearable systems, the implementation of low computational cost algorithms is the main solution to ensure real-time operation. The speed and the complexity of elaboration are important parameters to obtain the real-time detection. Reducing the computational complexity, it is possible to use less powerful systems with respect to personal computers (such as low-power micro-controllers). These devices can operate without connection to an external power supply, or using, for a very long time, a battery. In this context, not all the algorithms for the QRS detection can be implemented on wearable devices. The trade-off between the algorithm performance and the computational cost is an important issue that reduces the algorithm classes that can be used in this class of applications. In this case, the less complex class of algorithms (i.e., those with lower calculation time) is based on the use of signal derivatives and thresholds. In general, these algorithms are composed of two different parts, the pre-processing stage, for the noise removal, and the decision stage for the detection of the QRS complexes in ECG signal (Fig. 2).
Initially the user has to register his details with the application for the first time. The user has to enter the first name, last name, gender, etc. he also has to enter the userID and password for login purpose. So, registered user can login directly to use this application by using userID and password. Once the user registers, then he can use his userID and password to login in future. If the login is successful then home screen gets open.