Lih Chyun Shu

Mining frequent patterns in a varying-size sliding window of online transactional data streams

In some data stream applications, the information embedded in the data arriving in the most recent time period is of particular interest. This paper proposes a method for efficiently mining the frequent patterns in a varying-size sliding window of online data streams. To highlight recent frequent patterns in the data stream, a time decay model is used to differentiate the patterns of recently generated transactions from historical transactions. The derived concrete bounds of the decay factor can achieve either 100% recall or 100% precision. A summary data structure, named SWP-tree, is proposed for capturing the content of the transactions in the sliding window by scanning the stream only once. In order to speed up online processing of new transactions, the information of frequent patterns recorded in the SWP-tree is updated in an incrementally way. To make the mining operation efficient, the SWP-tree is periodically pruned by identifying insignificant patterns, which include two kinds of obsolete pattern and two kinds of infrequent pattern. Since the sliding window can change its size, the effect of window size is examined. The performance of the proposed technique is evaluated via simulation experiments. The results show that the proposed method is both efficient and scalable, and that it outperforms comparable algorithms.

Energy-Efficient Scheduling in Nonpreemptive Systems With Real-Time Constraints

In the past decade, the development of mobile and embedded systems has demanded energy efficiency for improving the lifetime of embedded devices. To avoid preemption overhead or ease timing verification, nonpreemptive scheduling has been deemed useful or necessary in meeting system timing requirements for certain applications built on embedded devices. In this paper, our aim is to design nonpreemptive scheduling algorithms that ensure timing correctness and optimize energy consumption on a processor with variable speeds. We propose a representative algorithm, ISA, which can produce lower speeds for a variety of nonpreemptive task sets than other comparable methods, and hence resulting in significant energy savings. When combined with a selective frequency-inheritance policy we design to efficiently determine if processor speedup can be disabled without jeopardizing any task deadlines, ISA can achieve even larger gains, up to 30% reduction in energy consumption. Finally, we propose a dynamic slack reclamation policy built on ISA, namely ISA-DR, which can result in additional energy savings when a task consumes less than its worst-case execution time.

Continuous reverse k nearest neighbor monitoring on moving objects in road networks

Continuous reverse k nearest neighbor (CRkNN) monitoring in road networks has recently received increasing attentions. However, there is still a lack of efficient CRkNN algorithms in road networks up to now. In road networks, moving query objects and data objects are restricted by the connectivity of the road network and both the object-query distance and object-object distance updates affect the result of CRkNN queries. In this paper, we present a novel algorithm for continuous and incremental evaluation of CRkNN queries in road networks. Our method is based on a novel data structure called dual layer multiway tree (DLM tree) we proposed to represent the whole monitoring region of a CRkNN query q. We propose several lemmas to reduce the monitoring region of q and the number of candidate objects as much as possible. Moreover, by associating a variable NN_count with each candidate object, we can simplify the monitoring of candidate objects. There are a large number of objects roaming in a road network and many of them are irrelevant to a specific CRkNN query of a query object q. To minimize the processing extension, for a road in the network, we give an IQL list and an IQCL list to specify the set of query objects and data objects whose location updates should be maintained for CRkNN processing of query objects. Our CRkNN method consists of two phase: the initial result generating phase and incremental maintenance phase. In each phase, algorithms with high performance are proposed to make our CRkNN method more efficient. Extensive simulation experiments are conducted and the result shows that our proposed approach is efficient and scalable in processing CRkNN queries in road networks.

CkNN query processing over moving objects with uncertain speeds in road networks

This paper focuses on processing continuous k nearest neighbor queries over objects moving at uncertain speeds (CUkNN) in road networks. We present a novel model to estimate the distances between objects and a query, both of which move at variable speeds in the road network. Based on the proposed distance model, we present a CUkNN query monitoring method to continuously find the objects that could potentially be the k-nearest neighbors (kNN) of the query. We propose an efficient method to calculate the probability of each object being a kNN of a query. The key thing about the method is that the probability of an object being a kNN of query q is shown to be equivalent to the probability of a special line segment being one of the k-nearest lines from q, which greatly simplifies the probability calculation.

Achieving Bounded and Predictable Recovery using Real-Time Logging

Real-time databases (RTDBs) are increasingly being used as an integral part of many computer systems. During normal operation, transactions in RTDBs must be executed in such a way that transaction timing and data time validity constraints can be met. RTDBs must also prepare for possible failures and provide fault tolerance capability. Principles for fault tolerance in RTDBs must take timing requirements into consideration and are distinct from those for conventional databases. We discuss these issues in this paper and describe a logging and recovery technique that is timecognizant and is suitable for an important class of RTDB applications. The technique minimizes normal runtime overhead caused by logging and has a predictable impact on transaction timing constraints. Upon a failure, the system can recover critical data to a consistent and temporally valid state within predictable time bounds. The system can then resume its major functioning while noncritical data are being recovered in the background. As a result, the recovery time is bounded and shortened. In short, we are providing a bounded and predictable logging and recovery technique for critical transactions accessing critical variant and invariant data, while the database may consist of all kinds of data. Our performance evaluation via simulation shows that logging overhead has a small effect on missing transaction deadlines while adding recovery capability. In addition, our algorithm scales well with respect to large I/O page sizes, indicating that it can be adapted to future generations of storage devices with larger I/O page sizes. We compare our algorithm with another approach that does more frequent checkpointing for data with certain special characteristics. Simulation results disclose conditions under which each approach performs better than the other. Experiments also show that recovery using our approach is 3–6 times faster than traditional recovery.

Scheduling Periodic Continuous Queries in Real-Time Data Broadcast Environments

On-demand broadcast is a promising data dissemination approach in mobile computing environments thanks to its adaptability and scalability for large-scale and dynamic workload. An important class of emerging data broadcast applications needs to monitor multiple time-varying data items continuously to be kept aware of the up-to-date information. This paper investigates the broadcast schedule problem for disseminating timely data to periodic continuous queries, and a systematic and highly efficient solution for applications of this type is provided. In particular, we propose a novel measure, called Bandwidth Utilization, to quantify the minimum bandwidth demand of a periodic continuous query set. The timing predictability can be ensured if a set of periodic continuous queries passes a bandwidth utilization based schedulability test. The schedulability test techniques are also extended to deal with dynamic query arrival and departure. An efficient online scheduling algorithm, called RM-UO, is developed, which can fulfill the timing constraints combined with the proposed query release and deletion policies. To demonstrate the effectiveness of theoretical results, an illustrative algorithm implementation is presented along with comprehensive performance analysis. Simulation results show that our solution offers nice timing predictability whereas other comparable best effort scheduling algorithms such as SIN-Q and DTIU experience different deadline miss ratios at different query workloads.

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.

A low-latency checkpointing scheme for mobile computing systems

Fault-tolerant mobile computing systems have different requirements and restrictions, not taken into account by conventional distributed systems. This paper presents a coordinated checkpointing scheme which reduces the delay involved in a global checkpointing process for mobile systems. A piggyback technique is used to track and record the checkpoint dependency information among processes during normal message transmission. During checkpointing, a concurrent checkpointing technique is designed to use the pre-recorded process dependency information to minimize process blocking time by sending checkpoint requests to dependent processes at once, hence saving the time to trace the dependency tree. Our checkpoint algorithm forces a minimum number of processes to take checkpoints. Via probability-based analysis, we show that our scheme can significantly reduce the latency associated with checkpoint request propagation, compared to traditional coordinated checkpointing approach.

Achieving bounded and predictable recovery using real-time logging

Real-time databases are increasingly being used as an integral part of many computer systems. During normal operation, transactions in real-time databases must be executed in such a way that transaction timing and data time validity constraints can be met. Real-time databases must also prepare for possible failures and provide fault tolerance capability. Principles for fault tolerance in real-time databases must take timing requirements into consideration and are distinct from those for conventional databases. We discuss these issues in this paper and describe a logging and recovery technique that is time-cognizant and is suitable for an important class of real-time database applications. The technique minimizes normal runtime overhead caused by logging and has a predictable impact on transaction timing constraints. Upon a failure, the system can recover critical data to a consistent and temporally valid state within predictable time bounds. The system can then resume its major functioning, while non-critical data is being recovered in the background. As a result, the recovery time is bounded and shortened. Our performance evaluation via simulation shows that logging overhead has a small effect on missing transaction deadlines while adding recovery capability. Experiments also show that recovery using our approach is 3 to 6 times faster than traditional recovery.

Searching continuous nearest neighbors in road networks on the air

Recently, people have begun to deal with location-based queries (LBQs) under broadcast environments. To the best of our knowledge, most of the existing broadcast-based LBQ methods are aimed at Euclidean spaces and cannot be readily extended to road networks. This paper takes the first step toward processing Continuous Nearest Neighbor queries in road Networks under wireless Broadcast environments (CN^3B). Our method leverages the key properties of Network Voronoi Diagram (NVD). We first present an efficient method to partition the NVD structure of the underlying road networks into a set of grid cells and number the grid cells obtained, based on which we further propose an NVD-based Distributed air Index (NVD-DI) to support CN^3B query processing. Finally, we propose an efficient algorithm on the client side to process CN^3B queries. Extensive simulation experiments have been conducted to demonstrate the efficiency of our approach. The results show that our proposed method is about 4 and 7.6 times more efficient than a less-sophisticated D-tree air index based method, in access latency and tuning time, respectively.