waashk/dblp huggingface dataset:
incremental stock time series data delivery and visualization. sb-tree is a binary tree data structure proposed to represent time series according to the importance of data points. its use in stock data management is distinguished by preserving the critical data points' attribute values, retrieving time series data according to the importance of data points and facilitating multi-resolution time series retrieval. as new stock data are available continuously, an effective updating mechanism for sb-tree is needed. in this paper, a study of different updating approaches is reported. three families of updating methods are proposed. they are periodic rebuild, batch update and point-by-point update. their efficiency, effectiveness and characteristics are compared and reported.
index compression vs. retrieval time of inverted files for xml documents. query languages for retrieval of xml documents allow for conditions referring both to the content and the structure of documents. in this paper, we investigate two different approaches for reducing index space of inverted files for xml documents. first, we consider methods for compressing index entries. second, we develop the new xs tree data structure which contains the structural description of a document in a rather compact form, such that these descriptions can be kept in main memory. experimental results on two large xml document collections show that very high compression rates for indexes can be achieved, but any compression increases retrieval time. on the other hand, highly compressed indexes may be feasible for applications where storage is limited, such as in pdas or e-book devices.
evaluating continuous nearest neighbor queries for streaming time series via pre-fetching. for many applications, it is important to quickly locate the nearest neighbor of a given time series. when the given time series is a streaming one, nearest neighbors may need to be found continuously at all time positions. such a standing request is called a continuous nearest neighbor query. this paper seeks fast evaluation of continuous queries on large databases. the initial strategy is to use the result of one evaluation to restrict the search space for the next. a more fundamental idea is to extend the existing indexing methods, used in many traditional nearest neighbor algorithms, with pre-fetching. specifically, pre-fetching is to predict the next value of the stream before it arrives, and to process the query as if the predicted value were the real one in order to load the needed index pages and time series into the allocated cache memory. furthermore, if the pre-fetched candidates cannot fit into the cache memory, they are stored in a sequential file to facilitate fast access to them. experiments show that pre-fetching improves the response time greatly over the direct use of traditional algorithms, even if the caching provided by the operating system is taken into consideration.
taxonomies by the numbers: building high-performance taxonomies. in this paper, we describe a system for the construction of taxonomies which yield high accuracies with automated categorization systems, even on web and intranet documents. in particular, we describe the way in which measurement of five key features of the system can be used to predict when categories are sufficiently well defined to yield high accuracy categorization. we describe the use of this system to construct a large (8800-category) general-purpose taxonomy and categorization system.
motion adaptive indexing for moving continual queries over moving objects. this paper describes a <i>motion adaptive</i> indexing scheme for efficient evaluation of moving continual queries (mcqs) over moving objects. it uses the concept of <i>motion-sensitive bounding boxes</i> (<i>msb</i>s) to model moving objects and moving queries. these bounding boxes automatically adapt their sizes to the dynamic motion behaviors of individual objects. instead of indexing frequently changing object positions, we index less frequently changing object and query <i>msb</i>s, where updates to the bounding boxes are needed only when objects and queries move across the boundaries of their boxes. this helps decrease the number of updates to the indexes. more importantly, we use <i>predictive query results</i> to optimistically precalculate query results, decreasing the number of searches on the indexes. motion-sensitive bounding boxes are used to incrementally update the predictive query results. our experiments show that the proposed motion adaptive indexing scheme is efficient for the evaluation of moving continual range queries.
adaptive load shedding for windowed stream joins. we present an adaptive load shedding approach for windowed stream joins. in contrast to the conventional approach of dropping tuples from the input streams, we explore the concept of selective processing for load shedding. we allow stream tuples to be stored in the windows and shed excessive cpu load by performing the join operations, not on the entire set of tuples within the windows, but on a dynamically changing subset of tuples that are learned to be highly beneficial. we support such dynamic selective processing through three forms of runtime adaptations: adaptation to input stream rates, adaptation to time correlation between the streams and adaptation to join directions. indexes are used to further speed up the execution of stream joins. experiments are conducted to evaluate our adaptive load shedding in terms of output rate. the results show that our selective processing approach to load shedding is very effective and significantly outperforms the approach that drops tuples from the input streams.
browsing large digital library collections using classification hierarchies. summarization of intermediary query result sets plays an important role when users browse through digital library collections. summarization enables users to quickly digest the results of their queries, and provides users with important information they can use to narrow their search interactively. techniques from the field of data analysis may be applied to the problem of generating summaries of query results efficiently. such techniques should permit the incorporation of classification hierarchies in order to provide powerful browsing environments for digital library users.
collective multi-label classification. common approaches to multi-label classification learn independent classifiers for each category, and employ ranking or thresholding schemes for classification. because they do not exploit dependencies between labels, such techniques are only well-suited to problems in which categories are independent. however, in many domains labels are highly interdependent. this paper explores multi-label conditional random field (crf)classification models that directly parameterize label co-occurrences in multi-label classification. experiments show that the models outperform their single-label counterparts on standard text corpora. even when multi-labels are sparse, the models improve subset classification error by as much as 40%.
management of disk space with rebate. the past decade has witnessed a proliferation of respositories whose workload consists of queries that retrieve information. these repositories provide on-line access to vast amount of data and serve as an integral component of many application domains (e.g., library information systems, scientific applications, entertainment industry). their storage subsystem is expected to be hierarchical consisting of memory, disk drives, and one or more tertiary storage devices. the database resides permanently on the tertiary storage devices and objects are swapped onto the magnetic disk drives on demand (and deleted once the disk storage capacity is exhausted). this may fragment the disk space over a period of time, resulting in a non-contiguous layout of an object across the surface of a disk drive. this is undesirable because, once the object is referenced, the disk drive is required to reposition its read head multiple times (incur seek operations) when retrieving the object, resulting in a low performance.this paper presents the design of rebate. rebate ensures the contiguous layout of each object across the surface of a disk drive by partitioning the available disk space into regions where each region manages objects of approximately the same size. we describe the tradeoffs of using rebate and its possible limitations.
a comparison of alternative continuous display techniques with heterogeneous multi-zone disks. a number of recent technological trends have made data intensive applications such as continuous media (audio and video) servers a reality. these servers are expected to play an important role in applications such as video-on-demand, digital library, news-on-demand, distance learning, etc. continuous media applications are data intensive and might require storage subsystems that consist of hundreds of (multi-zone) disk drives. with the current technological trends, a homogeneous disk subsystem might evolve to consist of a heterogeneous collection of disk drives. given such a storage subsystem, the system must continue to support a hiccup-free display of audio and video clips. this study describes extensions of four continuous display techniques for multi-zone disk drives to a heterogeneous platform. these techniques include ibm's logical track [21], hp's track pairing [4], and usc's fixb [9] and deadline driven techniques [10]. we quantify the performance tradeoff associated with these techniques using analytical models and simulation studies. the obtained results demonstrate tradeoffs between the cost per simultaneous stream supported by a technique, the wasted disk space, and the incurred startup latency.
mining the web to create minority language corpora. the web is a valuable source of language specific resources but the process of collecting, organizing and utilizing these resources is difficult. we describe corpusbuilder, an approach for automatically generating web-search queries for collecting documents in a minority language. it differs from pseudo-relevance feedback in that retrieved documents are labeled by an automatic language classifier as relevant or irrelevant, and this feedback is used to generate new queries. we experiment with various query-generation methods and query-lengths to find inclusion/exclusion terms that are helpful for retrieving documents in the target language and find that using odds-ratio scores calculated over the documents acquired so far was one of the most consistently accurate query-generation methods. we also describe experiments using a handful of words elicited from a user instead of initial documents and show that the methods perform similarly. experiments applying the same approach to multiple languages are also presented showing that our approach generalizes to a variety of languages.
speech user interfaces for information retrieval. the research proposed here concentrates on the problem of designing and developing a spoken query retrieval (sqr) system to access large document databases via voice. the main challenge is to identify and address issues related to designing an effective and efficient speech user interface (sui), especially if the aim is to facilitate spoken queries of large document databases. furthermore, the task of presenting large query result sets aurally should be performed such that the user's short term memory is not overloaded. in this paper, a framework allowing information retrieval to large document databases via voice is presented and findings from a research study using the framework will be discussed as well.
visualization of communication patterns in collaborative innovation networks - analysis of some w3c working groups. collaborative innovation networks (coins) are groups of self-motivated individuals from various parts of an organization or from multiple organizations, empowered by the internet, who work together on a new idea, driven by a common vision. in this paper we report first results of a project that examines innovation networks by analyzing the e-mail archives of some w3c (www consortium) working groups. these groups exhibit ideal characteristics for our purpose, as they form truly global networks working together over the internet to develop next generation technologies. we first describe the software tools we developed to visualize the temporal communication flow, which represent communication patterns as directed acyclic graphs, we then show initial results, which revealed significant variations between the communication patterns and network structures of the different groups., we were also able to identify distinctive communication patterns among group leaders, both those who were officially appointed and other who were assuming unofficial coordinating roles.
architecture of a metasearch engine that supports user information needs. when a query is submitted to a metasearch engine, decisions are made with respect to the underlying search engines to be used, what modifications will be made to the query, and how to score the results. these decisions are typically made by considering only the user's keyword query, neglecting the larger information need. users with specific needs, such as &ldquo;research papers&rdquo; or &ldquo;homepages,&rdquo; are not able to express these needs in a way that affects the decisions made by the metasearch engine. in this paper, we describe a metasearch engine architecture that considers the user's information need for each decision. users with different needs, but the same keyword query, may search different sub-search engines, have different modifications made to their query, and have results ordered differently. our architecture combines several powerful approaches together in a single general purpose metasearch engine.
inferring hierarchical descriptions. we create a statistical model for inferring hierarchical term relationships about a topic, given only a small set of example web pages on the topic, without prior knowledge of any hierarchical information. the model can utilize either the full text of the pages in the cluster or the context of links to the pages. to support the model, we use "ground truth" data taken from the category labels in the open directory. we show that the model accurately separates terms in the following classes: self terms describing the cluster, parent terms describing more general concepts, and child terms describing specializations of the cluster. for example, for a set of biology pages, sample parent, self, and child terms are science, biology, and genetics respectively. we create an algorithm to predict parent, self, and child terms using the new model, and compare the predictions to the ground truth data. the algorithm accurately ranks a majority of the ground truth terms highly, and identifies additional complementary terms missing in the open directory.
svm binary classifier ensembles for image classification. we study how the svm-based binary classifiers can be effectively combined to tackle the multi-class image classification problem. we study several ensemble schemes, including opc (one per class), pwc (pairwise coupling), and ecoc (error-correction output coding), that aim to achieve good error correction capability through redundancy. to enhance these ensemble schemes' accuracy, we propose methods that on the one hand boost the margins (i.e., confidence) of the svm-based binary classifiers, and, on the other hand, remove the noise of irrelevant classifiers from class prediction. from empirical study we show that our margin boosting and noise reduction methods lead to higher classification accuracy than ensemble schemes that are solely designed for maximum error correction capability.
context interchange: overcoming the challenges of large-scale interoperable database systems in a dynamic environment. research in database interoperability has primarily focused on circumventing schematic and semantic incompatibility arising from autonomy of the underlying databases. we argue that, while existing integration strategies might provide satisfactory support for small or static systems, their inadequacies rapidly become evident in large-scale interoperable database systems operating in a dynamic environment. this paper highlights the problem of receiver heterogeneity, scalability, and evolution which have received little attention in the literature, provides an overview of the context interchange approach to interoperability, illustrates why this is able to better circumvent the problems identified, and forges the connections to other works by suggesting how the context interchange framework differs from other integration approaches in the literature.
on off-topic access detection in information systems. we focus on detecting insider access violations to off-topic documents. previously, we utilized information retrieval techniques, e.g., clustering and relevance feedback, to warn of potential misuse. for the relevance feedback approach, we minimize the indicative features needed for detection using data mining techniques. we show that the derived reduced feature subset achieves equivalent performance to that of the previously derived full set of features.
bootstrapping for example-based data extraction. the effortless generation of wrappers for web data sources is a crucial task if proper access to the huge amount of semi-structured data on the web is to be granted. in particular, the development of strategies for wrapper generation based on user-given examples is currently one of the most promising research directions in web data extraction. in this paper we show how to use a pre-existing data repository to automatically generate examples and allow full automated example-based data extraction. to demonstrate the feasibility of our approach we provide a number of results obtained from experiments we carried out and discuss how our ideas can be used to improve extraction rates and for providing resilience and adaptiveness for example-based generated wrappers.
framework and algorithms for trend analysis in massive temporal data sets. mining massive temporal data streams for significant trends, emerging buzz, and unusually high or low activity is an important problem with several commercial applications. in this paper, we propose a framework based on relational records and metric spaces to study such problems. our framework provides the necessary mathematical underpinnings for this genre of problems, and leads to efficient algorithms in the stream/sort model of massive data sets (where the algorithm makes passes over the data, computes a new stream on the fly, and is allowed to sort the intermediate data). our algorithm makes novel use of metric approximations in the data stream context, and highlights the role of hierarchical organization of large data sets in designing efficient algorithms in the stream/sort model.
a probabilistic description-oriented approach for categorizing web documents. the automatic categorisation of web documents is becoming crucial for organising the huge amount of information available in the internet. we are facing a new challenge due to the fact that web documents have a rich structure and are highly heterogeneous. two ways to respond to this challenge are (1) using a representation of the content of web documents that captures these two characteristics and (2) using more effective classifiers.our categorisation approach is based on a probabilistic description-oriented representation of web documents, and a probabilistic interpretation of the k-nearest neighbour classifier. with the former, we provide an enhanced document representation that incorporates the structural and heterogeneous nature of web documents. with the latter, we provide a theoretical sound justification for the various parameters of the k-nearest neighbour classifier.experimental results show that (1) using an enhanced representation of web documents is crucial for an effective categorisation of web documents, and (2) a theoretical interpretation of the k-nearest neighbour classifier gives us improvement over the standard k-nearest neighbour classifier.
powerdb-ir - information retrieval on top of a database cluster. our current concern is a scalable infrastructure for information retrieval (ir) with up-to-date retrieval results in the presence of frequent, continuous updates. timely processing of updates is important with novel application domains, e.g., e-commerce. we want to use off-the-self hardware and software as much as possible. these issues are challenging, given the additional requirement that the resulting system must scale well. we have built powerdb-ir, a system that has the characteristics sought. this paper describes its design, implementation, and evaluation. powerdb-ir is a coordination layer for a database cluster. the rationale behind a database cluster is to 'scale-out', i.e., to add further cluster nodes, whenever necessary for better performance. we build on ir-to-database mappings and service decomposition to support high-level parallelism. we follow a three-tier architecture with the database cluster as the bottom layer for storage management. the middle tier provides ir-specific processing and update services. powerdb-ir has the following features: it allows to insert and retrieve documents concurrently, and it ensures freshness with almost no overhead. alternative physical data organization schemes provide adequate performance for different workloads. query processing techniques for the different data organizations efficiently integrate the ranked retrieval results from the cluster nodes. we have run extensive experiments with our prototype using commercial database systems and middleware software products. the main result is that powerdb-ir shows surprisingly ideal scalability and low response times.
xmltm: efficient transaction management for xml documents. a common approach to storage and retrieval of xml documents is to store them in a database, together with materialized views on their content. the advantage over "native" xml storage managers seems to be that transactions and concurrency are for free, next to other benefits. but a closer look and preliminary experiments reveal that this results in poor performance of concurrent queries and updates. the reason is that database lock contention hinders parallelism unnecessarily. we therefore investigate concurrency control at the semantic, i.e., xml level and describe a respective transaction manager xmltm. it features a new locking protocol dglock. it generalizes the protocol for locking on directed acyclic graphs by adding simple predicate locking on the content of elements, e.g., on their text. instead of using the original xml documents, we propose to take advantage of an abstraction of the xml document collection known as dataguides. xmltm allows to run xml processing at the underlying database at low ansi isolation degrees and to release database locks early without sacrificing correctness in this setting. we have built a complete prototype system that is implemented on top of the xml extender for ibm db2. our evaluation shows that our approach consistently yields performance improvements by an order of magnitude. we stress that our approach can also be implemented within a native xml storage manager, and we expect even better performance.