waashk/dblp huggingface dataset:
a customizable hybrid approach to data clustering. most current data clustering algorithms in data mining are based on a distance calculation in certain metric space. for spatial database systems (sdbs), the euclidean distance between two data points is often used to represent the relationship between data points. however, in some spatial settings and many other applications, distance alone is not enough to represent all the attributes of the relation between data points. we need a more powerful model to record more relational information between data objects. this paper adopts a graph model by which a database is regarded as a graph: each vertex of the graph represents a data point, and each edge, weighted or unweighted, is used to record the relation between two data points connected by the edge. based on the graph model, this paper presents a set of cluster analysis criteria to guide data clustering. the criteria can be used to measure clustering results and help improving the quality of clustering. further, a customizable algorithm using the criteria is proposed and implemented. this algorithm can produce clusters according to users' specifications. preliminary experiments show encouraging results.
implementing private vickrey auctions. vickrey auctions have the interesting property of eliminating any incentive for bidders to bid values that are different from their reserve prices (i.e., the true value they give to the item being auctioned). for several reasons, it is desirable that bidders keep private their reserve price. in [2] a protocol to implement a vickrey auction was presented. its main feature is that bids are kept private without the necessity of any trusted third party. in particular, after the auction is finished only the value of the second highest bid and the identity of the highest bidder are publicly revealed. however, in that paper several questions about the applicability of the protocol were left unanswered. in particular, all the presentation was theoretical and no implementation was provided. besides, the analysis of collusion risk was too brief. in this paper we address these issues in a deeper way. in addition, we present and analyze an implementation of the protocol, and we consider its practical applicability.
graphzip: a fast and automatic compression method for spatial data clustering. spatial data mining presents new challenges due to the large size and the high dimensionality of spatial data. a common approach to such challenges is to perform some form of compression on the initial databases and then process the compressed data. this paper presents a novel spatial data compression method, called graphzip, to produce a compact representation of the original data set. graphzip has two advantages: first, the spatial pattern of the original data set is preserved in the compressed data. second, arbitrarily dimensional data can be processed efficiently and automatically. applying graphzip to huge databases can enhance both the effectiveness and the efficiency of spatial data clustering. on one hand, performing a clustering algorithm on the compressed data set requires less running time while the pattern can still be discovered. on the other hand, the complexity of clustering is dramatically reduced. a general hierarchical clustering method using graphzip is proposed in this paper. the experimental studies on four benchmark spatial data sets produce very encouraging results.
the role of visualization in effective data cleaning. using visualization techniques to assist conventional data mining tasks has attracted considerable interest in recent years. this paper addresses a challenging issue in the use of visualization for data mining: choosing appropriate parameters for spatial data cleaning methods. on one hand, algorithm performance is improved through visualization. on the other hand, characteristics and properties of methods and features of data are visualized as feedbacks to the user. a 3-d visualization model, called waterfall, is proposed to assist spatial data cleaning in four important aspects: dimension-independent data visualization, visualization of data quality, algorithm parameter selection, and measurement of noise removing methods on parameter sensitiveness.
spatial contextual noise removal for post classification smoothing of remotely sensed images. extracting accurate land use and land cover information from remote sensing data is a challenging problem due to the gap between theoretically available information in remote sensing imagery and the limited classification ability based on spectral analysis. traditional classification techniques based on spectral analysis of single pixel usually produce "noisy" results that contain many wrongly classified pixels. this paper presents a novel post classification method to detect the pixels that are wrongly classified and reassign them to correct fields in spatial context. the strategy is demonstrated through the classification of a benchmark digital aerial photograph. the experimental results show that the proposed approach can produce a more accurate classification than previous approaches.
development of a collaborative environment applied to pediatric oncology. this project aims to brighten up those problems in the brazilian area of pediatric oncology, making possible the simultaneous collaboration of medical information between professionals of childhood cancer remotely located. it's the implementation of a collaborative environment in which the users can benefit itself with resources as chat, video-conference, bi-dimensional collaborative blackboard and collaborative volumetric visualization of medical images, also being able to get second medical opinion for diagnosis at distance for one or more cases of childhood cancer.
reliability in three-tier systems without application server coordination and persistent message queues. when dealing with fault tolerance in three-tier systems, two major problems need to be addressed, that is how to prevent duplicate transaction executions when classical timeout based retransmission logics are employed, and how to ensure the agreement among the back-end databases despite failures (a transaction needs to be aborted or committed at all the involved databases independently of the failure scenario). in this paper we address these problems by proposing a fault tolerant protocol that, unlike previous solutions, (i) avoids the additional phase of storing the client request into a persistent message queue and (ii) avoids explicit coordination of middle tier application servers (during both normal behavior and fail-over). our protocol reduces therefore the overhead imposed on the end-to-end interaction, thus improving user perceived responsiveness, and provides better scalability.
strudel: supporting trust in the dynamic establishment of peering coalitions. the coalition peering domain (cpd) is a recent innovation within the field of mesh networking. it facilitates the management of community-area networks in a distributed and scalable form, allowing devices to pool their network resources (particularly egress links) to the common good. however, as in p2p systems, this form of cooperative sharing architecture raises significant concerns about the effect of free-riders: nodes that utilise the bandwidth of others without providing an adequate return to the community. to address this problem, we propose strudel, a distributed framework that tackles the problem of free-riders and consists of: (i) a mechanism for the detection of malicious peers; (ii) a formal bayesian trust model, to assess peers' trustworthiness; (iii) a forwarding mechanism based on the maximisation of trust-informed utility.
hybrid log segmentation for assured damage assessment. a database log is the primary resource for damage assessment and recovery after an electronic attack. the log is a sequential file stored in the secondary storage and it can grow to humongous proportions in course of time. to make the process of damage assessment and recovery more efficient, segmenting the log based on different criteria has been proposed before. but the trade off is that, either segmenting the log involves a lot of computation or damage assessment is a complicated process. in this research we propose to strike a balance through hybrid log segmentation. our method will reduce the time taken to perform damage assessment while still segmenting the log fast enough so that no intricate computation is necessary. we build our model from a log that was previously segmented based on number of transactions, a time window for transactions to commit or space occupied by committed transactions. while performing damage assessment, we re-segment the log based on transaction dependency. thus during repeated damage assessment procedures, we create new segments with dependent transactions in them so that the process of damage assessment becomes faster when there are repeated attacks on the system. we have discussed various cases that are applicable and also presented algorithms for each of the cases discussed.
orchestrating document-based workflows with x-folders. this paper introduces x-folders: a software environment for multi-party document-based processes that aims at supporting the implementation of workflow processes involving multiple users that interact by means of documents stored in special, reactive, folders. when the documents inside a folder reach a given state a task is triggered, tasks orchestrate a set of web services in order to enact the workflow. x-folders is based on a model that promotes the implementation of applications distributed among peer sites and is based on internet standards: the document folders are accessible via webdav and via soap, external components can be invoked as web services; this enhances the interoperability of the workflow processes with external components and makes it possible to interact with the system even without specific clients.
an optimized approach for knn text categorization using p-trees. the importance of text mining stems from the availability of huge volumes of text databases holding a wealth of valuable information that needs to be mined. text categorization is the process of assigning categories or labels to documents based entirely on their contents. formally, it can be viewed as a mapping from the document space into a set of predefined class labels (aka subjects or categories); f: d&larr; {c1, c2...cn} where f is the mapping function, d is the document space and {c1, c2...cn} is the set of class labels. given an unlabeled document d, we need to find its class label, ci, using the mapping function f where f(d) = ci. in this paper, an optimized k-nearest neighbors (knn) classifier that uses intervalization and the p-tree1 technology to achieve a high degree of accuracy, space utilization and time efficiency is proposed: as new samples arrive, the classifier finds the k nearest neighbors to the new sample from the training space without a single database scan.
incremental interactive mining of constrained association rules from biological annotation data with nominal features. data arising from genomic and proteomic experiments is amassing at high speeds resulting in huge amounts of raw data; consequently, the need for analyzing such biological data --- the understanding of which is still lagging way behind --- has been prominently solicited in the post-genomic era we are currently witnessing. in this paper we attempt to analyze annotated genome data by applying a very central data-mining technique known as association rule mining with the aim of discovering rules capable of yielding deeper insights into this type of data. we propose a new technique capable of using domain knowledge in the form of queries in order to efficiently mine only the subset of the associations that are of interest to researcher in an incremental and interactive mode.
performance analysis framework for large software-intensive systems with a message passing paradigm. the launch of new features for mobile phones is increasing and the product life cycle symmetrically decreasing in duration as higher levels of sophistication are reached. therefore, the optimization of resources is particularly important in embedded systems where cpu power and memory space are limited. in this context, performance engineers must be able to predict and analyze the performance of the software architecture in order to support its evolution and its new requirements. in this paper i describe a framework for the analysis of the performance of a software architecture where the architectural elements communicate using message based communication services. using instrumentation traces i have extracted the run-time events i considered significant for the study. i have created a set of architectural views to reconstruct the dynamic and the static views of the architecture. understanding the connections and the relationships between them guide the performance analyst to a clear comprehension of how the architecture works and subsequently how it can be optimized. the performance analysis framework described constitute an essential set. the next challenge is to enhance the integration of the tools and the synchronization of the views and to facilitate the entry barrier to novice performance engineers.
replica selection in grid environment: a data-mining approach. grid technology is developed to share data across many organizations in different geographical locations. the idea of replication is to store data into different locations to improve data access performance. when different sites hold replicas, there are significant benefits realized when selecting the best replica. current research shows that both network bandwidth and disk i/o plays major role in file transfer. in this paper, we describe a new optimization technique that considers both disk throughput and network latencies when selecting the best replica. previous history of data transfer can help in predicting the best site that can hold replica. the k-nearest neighbor rule is one such predictive technique. in this technique, when a new request arrives for the best replica, it looks at all previous data to find a subset of previous file requests that are similar to it and uses them to predict the best site that can hold the replica. in this work, we implement and test k-nearest algorithm for various file access patterns and compare results with the traditional replica catalog based model. the results demonstrate that our model outperforms the traditional model for sequential and unitary random file access requests.
detecting identifiable areas in mobile environments. location-based applications and services are getting increasingly important for mobile users. they take into account a mobile user's current location and provide a location-dependent output. to support developers of location-based services, the nimbus framework hides specific details of positioning systems and provides uniform output containing physical as well as symbolic location information, which often are more suitable for applications and users. one basic problem solved by nimbus is the distributed location resolution problem: given a location; which identifiable areas cover this location, if the area information is distributed among different servers. this paper presents a solution of this problem that runs in the distributed, self-organizing nimbus infrastructure. the effectiveness is demonstrated with the help of performance analyses.
trust-decisions on the base of maximal information of recommended direct-trust. nowadays the concept of trust in computer communications starts to get more and more popular. while the idea of trust in human interaction seems to be obvious and understandable it is very difficult to find adequate and precise definitions of the trust-term. even more difficult is the attempt to find computable models of trust, particularly if one tries to keep all psycho-sociological morality from the real life out of the model. but, apart from all these problems, some approaches have been introduced more or less successful.in this paper, we do not create a new definition of trust. like many others we start with the simplest definition of trust as a probability of expected positive behaviour. our focus lies on the question, how far recommended trust-information is suitable to be the base of a trust-decision.our concept is based on the definition, that individual experiences are essential for a directional direct-trust relation between an entity and an opposite entity. recommendation-trust is a special direct-trust-relation. in order to be able to make trust-decisions on the base of recommended trust-information, our solution does not try to condense the chains of recommendation to only one value, but keeps the information untouched. we introduce trust-decisions as the final step of a randomly chosen path in a decision-tree where the weighted edges of the tree consist of recommended trust-values or the new introduced certainty-values, and of leafs with direct-trust-values or sections of total inexperience. a trust-decision is positive when a trust-threshold is exceeded by the determined value of the process. the calculation of the new introduced certainty-values, indicating the probability of the procedure to reach a direct-trust-value inside a sub-tree, plays a big part in this approach.one advantage of the procedure to induce the trust-decisions on the base of randomness lies in the higher resistance against false information from malicious entities because with a probability, paths through the tree will be chosen which exclude information of these entities.besides the new approach of trust-decisions on the base of recommended trust-information, we show how far (meaning with how many recommenders) it is reasonable to recommend trust-information. we will give suggestions how to optimize the tree of recommendation, certainty, and direct-trust, so that in an adequate time trust-decisions are possible and we show the influence of bad and malicious entities on the results of the trust-decision.
a distributed content-based search engine based on mobile code. current search engines crawl the web, download content, and digest this content locally. for multimedia content, this involves considerable volumes of data. furthermore, this process covers only publicly available content because content providers are concerned that they otherwise loose control over the distribution of their intellectual property. we present the prototype of our secure and distributed search engine, which dynamically pushes content based feature extraction to image providers. thereby, the volume of data that is transported over the network is significantly reduced, and the concerns mentioned above are alleviated. the distribution of feature extraction and matching algorithms is done by mobile software agents. we give a description of the search engine's architecture and implementation, quantitative evaluation results, and a discussion of related security mechanism for content protection and server security.
greedy heuristics and an evolutionary algorithm for the bounded-diameter minimum spanning tree problem. given a connected, weighted, undirected graph g and a bound d, the bounded-diameter minimum spanning tree problem seeks a spanning tree on g of lowest weight in which no path between two vertices contains more than d edges. this problem is np-hard for 4 < d < n - 1, where n is the number of vertices in g. an existing greedy heuristic for the problem, called ottc, is based on prim's algorithm. ottc usually yields poor results on instances in which the triangle inequality approximately holds; it always uses the lowest-weight edges that it can, but such edges do not in general connect the interior nodes of low-weight bounded-diameter trees. a new randomized greedy heuristic builds a bounded-diameter spanning tree from its center vertex or vertices. it chooses each next vertex at random but attaches the vertex with the lowest-weight eligible edge. this algorithm is faster than ottc and yields substantially better solutions on euclidean instances. an evolutionary algorithm encodes spanning trees as lists of their edges, augmented with their center vertices. it applies operators that maintain the diameter bound and always generate valid offspring trees. these operators are efficient, so the algorithm scales well to larger problem instances. on 25 euclidean instances of up to 1000 vertices, the ea improved substantially on solutions found by the randomized greedy heuristic.
hierarchical organization of a set of gaussian mixture speaker models for scaling up indexing and retrieval in audio documents. this work considers the case of spoken radio archives, in which a continuous ow of speech is introduced into the indexing system. to retrieval segments originating from a given speaker, scanning the set of enrolled speaker is needed. we propose a technique for organizing hierarchically the set of speaker models, to reduce the cost of speaker identification. this organization can be obtained and updated at low cost, thanks to a particular algorithm that exploits the gaussian mixture form of each speaker model. this parcimony is due to having computations carried out of model parameters rather than feature vectors. a first validation on real data illustrates the proposal.
a comparison of randomized and evolutionary approaches for optimizing base station site selection. it is increasingly important to optimally select base stations in the design of cellular networks, as customers demand cheaper and better wireless services. from a set of potential site locations, a subset needs to be selected which optimizes two critical objectives: service coverage and financial cost. as this is an np-hard optimization problem, heuristic approaches are required for problems of practical size. our approach consists of two phases which act upon a set of candidate site permutations at each generation. firstly, a sequential greedy algorithm is designed to commission sites from an ordering of candidate sites, subject to satisfying an alterable constraint. secondly, an evolutionary optimization technique, which is tested against a randomized approach, is used to search for orderings of candidate sites which optimize multiple objectives. the two-phase strategy is vigorously tested and the results delineated.
editorial message: special track on ubiquitous computing. ubiquitous computing places humans in the center of environments saturated with computing and wireless communications capabilities, yet gracefully integrated, so that technology recedes in the background of everyday activities. the ubiquitous computing world then, is a world largely defined by applications. but such applications present an altogether new set of requirements. the special track on ubiquitous computing applications, first introduced in acm sac 2004 and running for the second time in acm sac 2005, provides a forum for the discussion of all types of ubiquitous computing applications and related specialized infrastructures built for the deployment of targeted applications. individual papers place applications within their use context and introduce novel and appropriate interaction paradigms while at the same time addressing related technical and business aspects and consequently identify novel opportunities or constraints.