waashk/dblp huggingface dataset:
bluepoint: a bluetooth-based architecture for location-positioning services. mobile computing has induced a class of killer applications recently by extending the paradigm of parallel and distributed computing across a mobile environment. a very important class of applications involves the location-awareness of a mobile user. two major issues must be addressed to deliver appropriate services to mobile users. one is the availability of a good location-positioning system and the second is an infrastructure that enables the positions to be tracked for the perusal of user applications. in this paper, we propose the bluepoint architecture that allows mobile user locations to be managed effectively and transparently. this is realized through a middleware for the application developers, shielding the applications from the distribution and heterogeneity of the underlying communication networks and the locating infrastructures. the middleware offers a set of location-based support functions and high-level apis to the application programmers. other useful components are built around the architecture. we built a prototype of bluepoint, with some sample applications set in the context of a hospital environment. in this paper, we will focus on the middleware-based architecture of bluepoint, providing services which are integrated seamlessly to the user applications. services provided in the middleware include location information management, location information retrieval, location event notification and access control on mobile units.
multiple related document summary and navigation using concept hierarchies for mobile clients. mobile clients have limited display and navigation capabilities. to browse a set of documents, an intuitive method is to navigate through concept hierarchies. to reduce semantic loading for each term that represents the concepts and the cognitive loading of users due to the limited display, similar documents are grouped together before concept hierarchies are constructed for each document group. since the concept hierarchies only represent the salient concepts in the documents, term extraction is necessary. our pilot experiments showed that an unconventional combination of term frequency and inverse document frequency yielded similar performance (i.e. 71%) to previous work and the use of terms in titles achieved better performance than previous work (i.e. 82%). our preliminary results of building concept hierarchies after clustering compared to that without is encouraging (c.f. 82% and 67%). we believe that further research can enhance the performance of concept hierarchies to a level for commercial deployment for mobile clients.
image retrieval with embedded region relationships. image retrieval based on content from digital libraries, multimedia databases, the internet, and other sources has been an important problem addressed by several researchers. in this regard, one cannot overestimate the use of appropriate features such as color, texture, and shape. it has also become increasingly evident that the decomposition of images into regions is critical for useful results.in this paper we further study region-based image retrieval. we argue that a relationship between regions (such as a tiger amongst yellowish-green grass, or a plane against the blue sky with mountains in the background) is also important. our local segmentation algorithm is used to detect regions a priori. further, while searching for a match for an 'object' in the database, we allow for probabilistic 'multiple matches,' which are later pruned based on global consistent information. we provide a simple, fast algorithm implemented as an internet thin client connecting to a web server. experimental results indicate that our method has high precision, is robust towards translation, rotation, and scale changes, can handle partial occlusion, as well as many popular image transformations (such as shear and blur) much the way humans can.
event-driven scheduling for dynamic workload scaling in uniprocessor embedded systems. many embedded systems are designed to take timely reactions to the occurrences of interested scenarios. sometimes transient overloads might be experienced due to hardware malfunctions or workload bursts. thus a mechanism to focus system attention on urgent events could be a key to provide reasonably stable service. in this paper, we propose a new approach for workload scaling in uniprocessor real-time embedded systems. a deterministic algorithm is adopted to selectively fed hardware events into a system, and an event-driven task model is introduced to formulate complex precedence constraints among tasks. such a new approach removes the need for the adjustments of task periods and task phasing, which is crucial for many time-driven systems. the proposed approach was implemented in a real-time surveillance system, for which good accuracy and responsiveness were obtained under stressing workloads.
a new cell-based clustering method for large, high-dimensional data in data mining applications. recently data mining applications require a large amount of high-dimensional data. however, most clustering methods for data miming do not work efficiently for dealing with large, high-dimensional data because of the so-called 'curse of dimensionality'[1] and the limitation of available memory. in this paper, we propose a new cell-based clustering method which is more efficient for large, high-dimensional data than the existing clustering methods. our clustering method provides an efficient cell creation algorithm using a space-partitioning technique and uses a filtering-based index structure using an approximation technique. finally, we compare the performance of our cell-based clustering method with the clique method in terms of cluster construction time, precision, and retrieval time. the experimental results show that our clustering method achieves better performance on cluster construction time and retrieval time.
an efficient management scheme for large-scale flash-memory storage systems. flash memory is among the top choices for storage media in ubiquitous computing. with a strong demand of high-capacity storage devices, the usages of flash memory quickly grow beyond their original designs. the very distinct characteristics of flash memory introduce serious challenges to engineers in resolving the quick degradation of system performance and the huge demand of main-memory space for flash-memory management when high-capacity flash memory is considered. although some brute-force solutions could be taken, such as the enlarging of management granularity for flash memory, we showed that little advantage is received when system performance is considered. this paper proposes a flexible management scheme for large-scale flash-memory storage systems. the objective is to efficiently manage high-capacity flash-memory storage systems based on the behaviors of realistic access patterns. the proposed scheme could significantly reduce the main-memory usages without noticeable performance degradation.
enhanced object management for high performance web proxies. the dramatic increase of www traffic on the internet has led to the wide use of web proxy. the web proxies can be used to improve security, save network bandwidth and reduce network latency. however, as the network bandwidth increased, the general-purpose file system is rapidly becoming the performance bottleneck of web proxies. in this paper, we propose an enhanced object management, called unified, which is a user-level technique for improving the performance of web proxy. in unified method, several techniques are developed to improve the disk i/o performance. instead of the traditional trace-driven simulation, we employ polygraph 2.5.4 with polymix-3 workload to evaluate our system realistically. to investigate how the proxy performance depends on the equipped disk, we offer two sets of test machines. one is equipped with one ide disk and the other is equipped with five scsi disks. experimental results show that, in both tests, our method can improve the proxy performance dramatically by reducing the overhead associated with disk i/o.
developing component based adaptive applications in mobile environments. today, although the system supports for developing distributed applications become mature, they are inadequate for mobile environments where the runtime resources vary considerably or even disappear spontaneously. the objective of our work is to provide appropriate supports to facilitate the development of applications that adapt their behaviors to mobile environment changes. in this paper we present a framework based on a structured adaptive component model and two underlying middleware services, respectively, for monitoring environment variations and for coordinating adaptation actions of several components. we demonstrate the validity of our ideas through an adaptive video on demand application based on a prototype implementation of our framework. we also give the benefit and overhead of the proposed adaptation mechanisms.
a new algorithm for computing transitive closures. in this paper, we propose a new algorithm for computing transitive closures. it needs only o(e&middot;b) time and o(n&middot;b) space, where n represents the number of the nodes of a dag (directed acyclic graph), e the numbers of the edges, and b the dag's breadth.
a new approach for gene prediction using comparative sequence analysis. the availability of large fragments of genomic dna makes it possible to apply comparative genomics for identification of protein-coding regions. in this work, a comparative analysis is conducted on homologous genomic sequences of organisms with different evolutionary distances and the conservation of the non-coding regions between closely related organisms is found. in contrast, more distance shows much less intron similarity but less conservation on the exon structures. this study sought to illuminate the impact of evolutionary distances on the performance of the proposed gene-finding program based on the cross-species sequence comparison. base on the finding from comparative study and training of data sets, we proposed a model by which coding sequence could be identified by comparing sequences of multiple species, both close and approximately distant. the reliability of the proposed method is evaluated in terms of sensitivity and specificity, and results are compared to those obtained by other popular gene prediction programs. provided sequences can be found from other species at appropriate evolutionary distances, this approach could be applied in newly sequenced organisms where no species-dependent statistical models are available.
similar_join: extending dbms with a bio-specific operator. existing sequence comparison software applications lack adequate automation, abstraction, performance, and flexibility. users need a new way of studying and applying sequence comparisons in the post-genomics era. we invented and developed a new bio-specific database management system (dbms) operator, similar_join, to abstract the labor-intensive batch sequence similarity search task into a syntactically concise database operation. we implemented the similar_join operator as part of a relational operator package. this implementation enabled us to write simple pl/sql scripts within the dbms to accomplish routine sequence similarity searches conveniently, for example, a "batch blast" that compares 7,000 human genes against 500,000 human expressed sequence tags (est) in a few hours. we also implemented a simple version of similar_join as a database operator in the extended data cartridge of oracle 8i object-relational dbms. when fully integrated into sql language extensions, we demonstrated this operator could enable biology users to achieve interesting complex biological queries previously impossible inside the dbms.
signature file hierarchies and signature graphs: a new index method for object-oriented databases. in this paper, we propose a new index structure for object-oriented databases. the main idea of this is a graph structure, called a signature graph, which is constructed over a signature file generated for a class and improves the search of a signature file dramatically. in addition, the signature files (accordingly, the signature graphs) can be organized into a hierarchy according to the nested structure (called the aggregation hierarchy) of classes in an object-oriented database, which leads to another significant improvements.
on the transitive closure representation and adjustable compression. a composite object represented as a directed graph (digraph for short) is an important data structure that requires efficient support in cad/cam, case, office systems, software management, web databases, and document databases. it is cumbersome to handle such objects in relational database systems when they involve ancestor-descendant relationships (or say, recursive relationships). in this paper, we present a new encoding method to label a digraph, which reduces the footprints of all previous strategies. this method is based on a tree labeling method and the concept of branchings that are used in graph theory for finding the shortest connection networks. a branching is a subgraph of a given digraph that is in fact a forest, but covers all the nodes of the graph. on the one hand, the proposed encoding scheme achieves the smallest space requirements among all previously published strategies for recognizing recursive relationships. on the other hand, it leads to a new algorithm for computing transitive closures for dags (directed acyclic graph) in o(e&middot;b) time and o(n&middot;b) space, where n represents the number of the nodes of a dag, e the numbers of the edges, and b the dag's breadth. the method can also be extended to graphs containing cycles. especially, based on this encoding method, a multi-level compression is developed, by means of which the space for the representation of a transitive closure can be reduced to o((b/dk)&middot;n), where k is the number of compression levels and d is the average outdegree of the nodes.
a complex biological database querying method. many biological information systems rely on relational database management systems (rdbms) to manage high-throughput biological data. while keeping these data well archived, organized, and integrated in a common repository is still a challenging task, performing complex queries, i.e., explorative and abstract ad hoc user questions in biology, is an even formidable task often substituted by writing complicated software programs. in this work, we propose a "complex query modeling" method to address the challenge of complex querying in biological domains. query modeling consists of four distinct but interdependent phases of activities: representation of high-level problems, transformation of problems into connected query interfaces, designing database query structures, and translating query plans into high-performing sql statements. at each stage, we use different notations and query modeling practices. using gene indexing project as a case study, we show that query modeling enables prototypical development of high-quality sql solutions to an inherently abstract and vague user query question, which requires genechip designers to sift through millions of database records, process data in dozens of steps, and make myriads of intermediate decisions. we believe our "complex query modeling" method is applicable to other bioinformatics domains with needs for complex database queries.
an automated negotiation mechanism based on co-evolution and game theory. the problems associated with current automated negotiation approaches are of little feasibility in practical industry applications. this paper describes a new method that combines a game theory approach and a co-evolutionary approach to support an effective negotiation model for agents to resolve conflict. under this proposed method, the agents without knowing the other agent's strategies and payoffs, produce an optimised resolution that complies nash equilibrium and pareto efficiency concepts. we use a finitely repeated prisoner's dilemma game to demonstrate the proposed method.
real-time task scheduling anomaly: observations and prevention. this research is motivated by the practical needs in the porting of embedded software over platforms and the well-known multiprocessor anomaly [2, 3]. in particular, we consider the task scheduling problem when the system configuration changes. we show that new violations of the timing constraints of tasks might occur even when a more powerful processor or device is adopted. the concept of scheduler stability and rules are then proposed to prevent scheduling anomaly from happening for task executions that might be involved with task synchronization or i/o access. finally, we explore policies in bounding the the duration time of scheduling anomaly.
a new method of generating synchronizable test sequences that detect output-shifting faults based on multiple uio sequences. the objective of testing is to determine the conformance between a system and its specification. when testing distributed systems, the existence of multiple testers brings out the possibility of synchronization problems among remote testers and the possibility that output-shifting faults go undetected. this paper proposes a new method of generating minimal synchronizable test sequences that detect output-shifting faults based on multiple uio sequences. the procedure of test generation involves two steps: constructing several auxiliary digraphs from a given specification and finding a rural chinese post tour (rcpt) in the resultant digraph. when constructing the auxiliary digraphs, different from all the former methods, we use vertices to denote transitions and edges to represent two consecutive transitions. in terms of property and application, the proposed method can construct a relatively simple digraph which makes test generation easily. after applying it to practice, we got hold of better results than the existing methods.
combining supervised and unsupervised monitoring for fault detection in distributed computing systems. fast and accurate fault detection is becoming an essential component of management software for mission critical systems. a good fault detector makes possible to initiate repair actions quickly, increasing the availability of the system. the contribution of this paper is twofold. first a new concept of supervised and unsupervised monitoring is proposed for system fault detection. we use a statistical method, canonical correlation analysis (cca), to model the contextual dependencies between system inputs u and internal behavior x. by means of cca, the space x is transformed into two subsets of variables, which are monitored in a supervised and unsupervised manner respectively. by doing so, our approach can reduce the false alarms resulting from unusual workload changes, and hence achieve high fault detection rate. second, in order to test the performance of our approach, we simulate a variety of system faults in a real e-commerce application based on the multi-tiered j2ee architecture. experimental results demonstrate that the cca based approach can detect injected failures at their early stages when unusual phenomenon is very weak, and hence contribute to enormous time and cost savings in managing large scale distributed systems.
profit-driven uniprocessor scheduling with energy and timing constraints. energy-aware scheduling has received much attention in recent years, especially for systems with serious considerations on energy consumption. while most previous work focuses on the minimization of energy consumption, this paper exploits the maximization of the entire system profit under energy and timing constraints. we propose a greedy approximation algorithm with a 2-approximation ratio. a fully polynomial time approximation scheme (fptas) is also proposed, which is an optimal approximation algorithm unless p = np. for each specified amount of error tolerant to users, the approximation algorithm could provide trade-offs among the specified error, the running time, the approximation ratio, and the memory space complexity. it provides ways for system engineers to trade performance with implementation constraints.
cluster rendering of skewed datasets via visualization. information visualization is commonly recognized as a useful method for understanding sophistication in large datasets. in this paper, we introduce a flexible clustering approach with visualization techniques, aiming at the datasets that have skewed cluster distribution. this paper has three contributions. first, we propose a framework vista that incorporates information visualization methods into the clustering process in order to enhance the understanding of the intermediate clustering results and allow user to revise the clustering results easily. second, we develop a visualization model that maps multidimensional dataset to 2d visualizations while preserving or partially preserving clusters. third, based on the visualization model, a set of operating rules are proposed to guide the user rendering clusters efficiently. experiments show that the vista system can yield lower error rates for real datasets than typical automated algorithms.
an integrated computational proteomics method to extract protein targets for fanconi anemia studies. fanconi anemia (fa) is a rare autosomal genetic disease with multiple birth defects and severe childhood complications for its patients. the lack of sequence homology of the entire fa complementation group proteins in such as fancc, fancg, fanca makes them extremely difficult to characterize using conventional bioinformatics methods. in this work, we describe how to use computational methods to extract protein targets for fa, using protein interaction data set collected for fanc group c protein (fancc). we first generated an initial set of 130 fa-interacting proteins as "fancc seed proteins" by merging an in-house experimental set of fancc tandem affinity purification (tap) pulldown proteomics data identified from mass spectrometry methods with publicly available human fancc-interacting proteins. next, we expanded the fancc seed proteins using a nearest-neighbor method to generate a fancc protein interaction subnetwork of 948 proteins in 903 protein interactions. we show that this network is statistically significant, with high indices of aggregation and separations. we also show a visualization of the network, support the evidence that many well-connected proteins exists in the network. further, we developed and applied an interaction network protein scoring algorithm, which allows us to calculate a ranked list of significant fa proteins. our result has been supporting further biological investigations of disease biologists on our team. we believe our method can be generalized to other disease biology studies with similar problems.
tree inclusion algorithm, signatures and evaluation of path-oriented queries. in this paper, a method to evaluate path-oriented queries in document databases is proposed. the main idea of this method is to handle the evaluation of a path-oriented query as a tree inclusion problem. a new algorithm for tree-inclusion is discussed, which integrates a top-down process into a bottom-up searching strategy. on the one hand, the algorithm can be arranged to access the data on disk page-wise and fits therefore within a database environment. on the other hand, the algorithm can be combined with the signature indexing technique to cut off useless subtree inclusion checking as early as possible. experiments have been conducted to compare this method with some existing approaches, which shows that the integration of the signatures into the top-down tree inclusion is highly promising.