bluuebunny/crossref_metadata_2025 huggingface dataset:
Main-memory scan sharing for multi-core CPUs
Authors: Qiao Lin , Raman Vijayshankar , Reiss Frederick , Haas Peter J. , Lohman Guy M.
Published: June 2014

Computer architectures are increasingly based on multi-core CPUs and large memories. Memory bandwidth, which has riot kept pace with the increasing number of cores, has become the primary processing bottleneck, replacing disk I/O as the limiting factor. To address this challenge, we provide novel algorithms for increasing the throughput of Business Intelligence (BI) queries, as well as for ensuring fairness and avoiding starvation among a concurrent set of such queries. To maximize throughput, we propose a novel FullSharing scheme that allows all concurrent queries, when performing base-table I/O, to share the cache belonging to a given core. We then generalize this approach to a BatchSharing scheme that avoids thrashing on "agg-tables" ---hash tables that are used for aggregation processing---caused by execution of too many queries on a core. This scheme partitions queries into batches such that the working-set of agg-table entries for each batch can fit into a cache; an efficient sampling technique is used to estimate selectivities and working-set sizes for purposes of query partitioning. Finally, we use lottery-scheduling techniques to ensure fairness and impose a hard upper bound on staging time to avoid starvation. On our 8-core testbed, we were able to completely remove the memory I/O bottleneck, increasing throughput by a factor of 2 to 2.5, while also maintaining fairness and avoiding starvation.
Multidimensional content eXploration
Authors: Simitsis Alkis , Baid Akanksha , Sismanis Yannis , Reinwald Berthold
Published: June 2014

Content Management Systems (CMS) store enterprise data such as insurance claims, insurance policies, legal documents, patent applications, or archival data like in the case of digital libraries. Search over content allows for information retrieval, but does not provide users with great insight into the data. A more analytical view is needed through analysis, aggregations, groupings, trends, pivot tables or charts, and so on. Multidimensional Content eXploration (MCX) is about effectively analyzing and exploring large amounts of content by combining keyword search with OLAP-style aggregation, navigation, and reporting. We focus on unstructured data or generally speaking documents or content with limited metadata, as it is typically encountered in CMS. We formally present how CMS content and metadata should be organized in a well-defined multidimensional structure, so that sophisticated queries can be expressed and evaluated. The CMS metadata provide traditional OLAP static dimensions that are combined with dynamic dimensions discovered from the analyzed keyword search result, as well as measures for document scores based on the link structure between the documents. In addition, we provide means for multidimensional content exploration through traditional OLAP rollupdrilldown operations on the static and dynamic dimensions, solutions for multi-cube analysis and dynamic navigation of the content. We present our prototype, called DBPubs, which stores research publications as documents that can be searched and -most importantly-- analyzed, and explored. Finally, we present experimental results of the efficiency and effectiveness of our approach.
Anonymizing bipartite graph data using safe groupings
Authors: Cormode Graham , Srivastava Divesh , Yu Ting , Zhang Qing
Published: June 2014

Private data often comes in the form of associations between entities, such as customers and products bought from a pharmacy, which are naturally represented in the form of a large, sparse bipartite graph. As with tabular data, it is desirable to be able to publish anonymized versions of such data, to allow others to perform ad hoc analysis of aggregate graph properties. However, existing tabular anonymization techniques do not give useful or meaningful results when applied to graphs: small changes or masking of the edge structure can radically change aggregate graph properties.

We introduce a new family of anonymizations, for bipartite graph data, called (
k, l
)-groupings. These groupings preserve the underlying graph structure perfectly, and instead anonymize the mapping from entities to nodes of the graph. We identify a class of "safe" (
k, l
)-groupings that have provable guarantees to resist a variety of attacks, and show how to find such safe groupings. We perform experiments on real bipartite graph data to study the utility of the anonymized version, and the impact of publishing alternate groupings of the same graph data. Our experiments demonstrate that (
k, l
)-groupings offer strong tradeoffs between privacy and utility.
Reasoning and identifying relevant matches for XML keyword search
Authors: Liu Ziyang , Cher Yi
Published: June 2014

Keyword search is a user-friendly mechanism for retrieving XML data in web and scientific applications. An intuitively compelling but vaguely defined goal is to identify matches to query keywords that are
relevant
to the user. However, it is hard to directly evaluate the relevance of query results due to the inherent ambiguity of search semantics. In this work, we investigate an axiomatic framework that includes two intuitive and non-trivial properties that an XML keyword search technique should ideally satisfy:
monotonicity
and
consistency
, with respect to data and query. This is the first work that reasons about keyword search strategies from a formal perspective.

Then we propose a novel semantics for identifying relevant matches, which, to the best of our knowledge, is the only existing algorithm that satisfies both properties. An efficient algorithm is designed for realizing this semantics. Extensive experimental studies have verified the intuition of the properties and shown the effectiveness of the proposed algorithm.
Discovery of convoys in trajectory databases
Authors: Jeung Hoyoung , Yiu Man Lung , Zhou Xiaofang , Jensen Christian S. , Shen Heng Tao
Published: June 2014

As mobile devices with positioning capabilities continue to proliferate, data management for so-called trajectory databases that capture the historical movements of populations of moving objects becomes important. This paper considers the querying of such databases for convoys, a convoy being a group of objects that have traveled together for some time.

More specifically, this paper formalizes the concept of a convoy query using density-based notions, in order to capture groups of arbitrary extents and shapes. Convoy discovery is relevant for real-life applications in throughput planning of trucks and carpooling of vehicles. Although there has been extensive research on trajectories in the literature, none of this can be applied to retrieve correctly exact convoy result sets. Motivated by this, we develop three efficient algorithms for convoy discovery that adopt the well-known filter-refinement framework. In the filter step, we apply line-simplification techniques on the trajectories and establish distance bounds between the simplified trajectories. This permits efficient convoy discovery over the simplified trajectories without missing any actual convoys. In the refinement step, the candidate convoys are further processed to obtain the actual convoys. Our comprehensive empirical study offers insight into the properties of the paper's proposals and demonstrates that the proposals are effective and efficient on real-world trajectory data.
A pay-as-you-go framework for query execution feedback
Authors: Chaudhuri Surajit , Narasayya Vivek , Ramamurthy Ravi
Published: June 2014

Past work has suggested that query execution feedback can be useful in improving the quality of plans by correcting cardinality estimation errors in the query optimizer. The state-of-the-art approach for obtaining execution feedback is "passive" monitoring which records the cardinality of each operator in the execution plan. We observe that there are many cases where even after repeated executions of the same query with use of feedback from passive monitoring, suboptimal choices in the execution plan cannot be corrected. We present a novel "pay-as-you-go" framework in which a query potentially incurs a small overhead on each execution but obtains cardinality information that is not available with passive monitoring alone. Such a framework can significantly extend the reach of query execution feedback in obtaining better plans. We have implemented our techniques in Microsoft SQL Server, and our evaluation on real world and synthetic queries suggests that plan quality can improve significantly compared to passive monitoring even at low overheads.
Mining non-redundant high order correlations in binary data
Authors: Zhang Xiang , Pan Feng , Wang Wei , Nobel Andrew
Published: June 2014

Many approaches have been proposed to find correlations in binary data. Usually, these methods focus on pair-wise correlations. In biology applications, it is important to find correlations that involve more than just two features. Moreover, a set of strongly correlated features should be non-redundant in the sense that the correlation is strong
only when all
the interacting features are considered together. Removing any feature will greatly reduce the correlation.

In this paper, we explore the problem of finding non-redundant high order correlations in binary data. The high order correlations are formalized using multi-information, a generalization of pairwise mutual information. To reduce the redundancy, we require any subset of a strongly correlated feature subset to be weakly correlated. Such feature subsets are referred to as Non-redundant Interacting Feature Subsets (NIFS). Finding all NIFSs is computationally challenging, because in addition to enumerating feature combinations, we also need to check all their subsets for redundancy. We study several properties of NIFSs and show that these properties are useful in developing efficient algorithms. We further develop two sets of upper and lower bounds on the correlations, which can be incorporated in the algorithm to prune the search space. A simple and effective pruning strategy based on pair-wise mutual information is also developed to further prune the search space. The efficiency and effectiveness of our approach are demonstrated through extensive experiments on synthetic and real-life datasets.
Keyword search on external memory data graphs
Authors: Dalvi Bhavana Bharat , Kshirsagar Meghana , Sudarshan S.
Published: June 2014

Keyword search on graph structured data has attracted a lot of attention in recent years. Graphs are a natural "lowest common denominator" representation which can combine relational, XML and HTML data. Responses to keyword queries are usually modeled as trees that connect nodes matching the keywords.

In this paper we address the problem of keyword search on graphs that may be significantly larger than memory. We propose a graph representation technique that combines a condensed version of the graph (the "supernode graph") which is always memory resident, along with whatever parts of the detailed graph are in a cache, to form a multi-granular graph representation. We propose two alternative approaches which extend existing search algorithms to exploit multigranular graphs; both approaches attempt to minimize IO by directing search towards areas of the graph that are likely to give good results. We compare our algorithms with a virtual memory approach on several real data sets. Our experimental results show significant benefits in terms of reduction in IO due to our algorithms.
Energy cost, the key challenge of today's data centers
Authors: Poess Meikel , Nambiar Raghunath Othayoth
Published: June 2014

Historically, performance and price-performance of computer systems have been the key purchasing arguments for customers. With rising energy costs and increasing power use due to the ever-growing demand for computing power (servers, storage, networks), electricity bills have become a significant expense for today's data centers. In the very near future, energy efficiency is expected to be one of the key purchasing arguments. Some performance organizations, such as SPEC, have developed power benchmarks for single servers (SPECpower_ssj2008), but so far, no benchmark exists that measures the power consumption of transaction processing systems. In this paper, we develop a power consumption model based on data readily available in the TPC-C full disclosure report of published benchmarks. We verify our model with measurements taken from three fully scaled and optimized TPC-C configurations including client (middle-tier) systems, database server, and storage subsystem. By applying this model to a subset of 7 years of TPC-C results, we identify the most power-intensive components and demonstrate the existing power consumption trends over time. Assuming similar trends in the future, the hardware enhancements alone will not be able to satisfy the demand for energy efficiency. In its outlook, this paper looks at potential hardware and software enhancements to meet the energy efficiency demands of future systems. Realizing the importance of energy efficiency, the Transaction Processing Performance Council (TPC) has formed a working group to look into adding energy efficiency metrics to all its benchmarks. This paper is expected to complement this initiative.
Industry-scale duplicate detection
Authors: Weis Melanie , Naumann Felix , Jehle Ulrich , Lufter Jens , Schuster Holger
Published: June 2014

Duplicate detection is the process of identifying multiple representations of a same real-world object in a data source. Duplicate detection is a problem of critical importance in many applications, including customer relationship management, personal information management, or data mining.

In this paper, we present how a research prototype, namely DogmatiX, which was designed to detect duplicates in hierarchical XML data, was successfully extended and applied on a large scale industrial relational database in cooperation with Schufa Holding AG. Schufa's main business line is to store and retrieve credit histories of over 60 million individuals. Here, correctly identifying duplicates is critical both for individuals and companies: On the one hand, an incorrectly identified duplicate potentially results in a false negative credit history for an individual, who will then not be granted credit anymore. On the other hand, it is essential for companies that Schufa detects duplicates of a person that deliberately tries to create a new identity in the database in order to have a clean credit history.

Besides the quality of duplicate detection, i.e., its effectiveness, scalability cannot be neglected, because of the considerable size of the database. We describe our solution to coping with both problems and present a comprehensive evaluation based on large volumes of real-world data.
Relational support for flexible schema scenarios
Authors: Acharya Srini , Carlin Peter , Galindo-Legaria Cesar , Kozielczyk Krzysztof , Terlecki Pawel , Zabback Peter
Published: June 2014

Efficient support for applications that deal with data heterogeneity, hierarchies and schema evolution is an important challenge for relational engines. In this paper we show how this flexibility can be handled in Microsoft SQL Server. For this purpose, the engine has been equipped in an integrated package of relational extensions. The package includes sparse storage, column set operations, filtered indices, filtered statistics and hierarchy querying with OrdPath labeling. In addition, economical loading of metadata allow us to answer queries independently of the number of columns in a table and drastically improve scaling capabilities. The design of a prototypical content and collaboration application based on a wide table is described, along with experiments validating its performance.
Towards a streaming SQL standard
Authors: Jain Namit , Mishra Shailendra , Srinivasan Anand , Gehrke Johannes , Widom Jennifer , Balakrishnan Hari , Çetintemel Uǧur , Cherniack Mitch , Tibbetts Richard , Zdonik Stan
Published: June 2014

This paper describes a unification of two different SQL extensions for streams and its associated semantics. We use the data models from Oracle and StreamBase as our examples. Oracle uses a time-based execution model while StreamBase uses a tuple-based execution model. Time-based execution provides a way to model simultaneity while tuple-based execution provides a way to react to primitive events as soon as they are seen by the system.

The result is a new model that gives the user control over the granularity at which one can express simultaneity. Of course, it is possible to ignore simultaneity altogether. The proposed model captures ordering and simultaneity through partial orders on batches of tuples. The batching and the ordering are encapsulated in and can be modified by means of a powerful new operator that we call SPREAD. This paper describes the semantics of SPREAD and gives several examples of its use.
P2P logging and timestamping for reconciliation
Authors: Tlili Mounir , Dedzoe W. Kokou , Pacitti Esther , Valduriez Patrick , Akbarinia Reza , Molli Pascal , Canals Gérôme , Laurière Stéphane
Published: June 2014

In this paper, we address data reconciliation in peer-to-peer (P2P) collaborative applications. We propose P2P-LTR (Logging and Timestamping for Reconciliation) which provides P2P logging and timestamping services for P2P reconciliation over a distributed hash table (DHT). While updating at collaborating peers, updates are timestamped and stored in a highly available P2P log. During reconciliation, these updates are retrieved in total order to enforce eventual consistency. In this paper, we first give an overview of P2P-LTR with its model and its main procedures. We then present our prototype used to validate P2P-LTR. To demonstrate P2P-LTR, we propose several scenarios that test our solutions and measure performance. In particular, we demonstrate how P2P-LTR handles the dynamic behavior of peers with respect to the DHT.
DObjects
Authors: Jurczyk Pawel , Xiong Li
Published: June 2014

Many contemporary applications rely heavily on large scale distributed and heterogeneous data sources. The key constraints for building a distributed data query infrastructure for such applications are: scalability, consistency, heterogeneity, and network and resource dynamics. We designed and developed DObjects, a general-purpose query and data operations infrastructure that can be integrated with metacomputing middleware. This demo proposal describes the architecture and the dynamic query processing functionalities of our data services and shows how they are integrated with a metacomputing framework offering users an open platform for building distributed applications that require access to data integrated from multiple data sources.
Semandaq
Authors: Fan Wenfei , Geerts Floris , Jia Xibei
Published: June 2014

We present Semandaq, a prototype system for improving the quality of relational data. Based on the recently proposed
conditional functional dependencies
(CFDs), it detects and repairs errors and inconsistencies that emerge as violations of these constraints. We demonstrate the following functionalities supported by Semandaq: (a) an interface for specifying CFDs; (b) a visual tool for automated detection of CFD violations in relational data, leveraging efficient SQL-based techniques; (c) extensive visual data exploration capabilities that provide the user with various measures of the quality of the data; (d) repair (cleaning) functionality without excess human interaction, built upon CFD-based cleaning algorithms; we show how Semandaq allows for a natural exploration of the quality of the obtained repairs. Semandaq is a promising tool that provides easy access and user-friendly data quality facilities for any relational database system.
When is it time to rethink the aggregate configuration of your OLAP server?
Authors: Hose Katja , Klan Daniel , Marx Matthias , Sattler Kai-Uwe
Published: June 2014

OLAP servers based on relational backends typically exploit materialized aggregate tables to improve response times of complex analytical queries. One of the key problems in this context is the view selection problem: choosing the optimal set of aggregation tables (called configuration) for a given workload. In this paper, we present a system that continuously monitors the workload and raises a quantified alert, when a better configuration is available. We address the tasks of query monitoring and view selection at the OLAP level instead of the SQL level, which simplifies the containment checks as well as rewriting and in this way helps to reduce the complexity of the backend system. At the demo we plan to show how our system works, i.e., how the system reacts upon arbitrary (interactive) workloads and how the user is alerted that a better configuration is available.
H-store
Authors: Kallman Robert , Kimura Hideaki , Natkins Jonathan , Pavlo Andrew , Rasin Alexander , Zdonik Stanley , Jones Evan P. C. , Madden Samuel , Stonebraker Michael , Zhang Yang , Hugg John , Abadi Daniel J.
Published: June 2014

Our previous work has shown that architectural and application shifts have resulted in modern OLTP databases increasingly falling short of optimal performance [10]. In particular, the availability of multiple-cores, the abundance of main memory, the lack of user stalls, and the dominant use of stored procedures are factors that portend a clean-slate redesign of RDBMSs. This previous work showed that such a redesign has the potential to outperform legacy OLTP databases by a significant factor. These results, however, were obtained using a bare-bones prototype that was developed just to demonstrate the potential of such a system. We have since set out to design a more complete execution platform, and to implement some of the ideas presented in the original paper. Our demonstration presented here provides insight on the development of a distributed main memory OLTP database and allows for the further study of the challenges inherent in this operating environment.
C-DEM
Authors: Guo Fan , Li Lei , Faloutsos Christos , Xing Eric P.
Published: June 2014

The amount of biological data publicly available has experienced an exponential growth as the technology advances. Online databases are now playing an important role as information repositories as well as easily accessible platforms for researchers to communicate and contribute. Recent research projects in image bioinformatics produce a number of databases of images, which visualize the spatial expression pattern of a gene (eg. "fj"), and most of which also have one or several annotation keywords (eg., "embryonic hindgut").

C-DEM is an online system for Drosophila (= fruit-fly) Embryo images Mining. It supports queries from all three modalities to all three, namely, (a) genes, (b) images of gene expression, and (c) annotation keywords of the images. Thus, it can find images that are similar to a given image, and/or related to the desirable annotation keywords, and/or related to specific genes. Typical queries are
what are most suitable keywords to assign to image insitu28465.jpg
or
find images that are related to gene "fj", and to the keyword "embryonic hindgut"
. C-DEM uses state-of-the-art feature extraction methods for images (wavelets and principal component analysis). It envisions the whole database as a tri-partite graph (one type for each modality), and it uses fast and flexible proximity measures, namely, random walk with restarts (RWR).

In addition to flexible querying, C-DEM allows for
navigation:
the user can click on the results of an earlier query (image thumbnails and/or keywords and/or genes), and the system will report the most related images (and keywords, and genes). The demo is on a real Drosophila Embryo database, with 10,204 images, 2,969 distinct genes, and 113 annotation keywords. The query response time is below one second on a commodity desktop.
Querying and monitoring distributed business processes
Authors: Milo Tova , Deutch Daniel
Published: June 2014

A business process (BP for short) consists of a group of business activities undertaken by one or more organizations in pursuit of some particular goal. It usually operates in a cross-organization, distributed environment and the software implementing it is fairly complex.
Standards
facilitate the design, deployment, and execution of BPs. In particular, the recent BPEL standard (Business Process Execution Language), provides an XML-based language to describe the interface between the participants in a process, as well as the full operational logic of the process and its execution flow. BPEL specifications are automatically compiled into executable code that implements the described BP and runs on a BPEL application server. Processes execution is traced, and their run-time behavior can be recorded in standard XML formats.
A revival of integrity constraints for data cleaning
Authors: Fan Wenfei , Geerts Floris , Jia Xibei
Published: June 2014

Integrity constraints,
a.k.a
. data dependencies, are being widely used for improving
the quality of schema
. Recently constraints have enjoyed a revival for
improving the quality of data
. The tutorial aims to provide an overview of recent advances in constraint-based data cleaning.
Querying and mining of time series data
Authors: Ding Hui , Trajcevski Goce , Scheuermann Peter , Wang Xiaoyue , Keogh Eamonn
Published: June 2014

The last decade has witnessed a tremendous growths of interests in applications that deal with querying and mining of time series data. Numerous representation methods for dimensionality reduction and similarity measures geared towards time series have been introduced. Each individual work introducing a particular method has made specific claims and, aside from the occasional theoretical justifications, provided quantitative experimental observations. However, for the most part, the comparative aspects of these experiments were too narrowly focused on demonstrating the benefits of the proposed methods over some of the previously introduced ones. In order to provide a comprehensive validation, we conducted an extensive set of time series experiments re-implementing 8 different representation methods and 9 similarity measures and their variants, and testing their effectiveness on 38 time series data sets from a wide variety of application domains. In this paper, we give an overview of these different techniques and present our comparative experimental findings regarding their effectiveness. Our experiments have provided both a unified validation of some of the existing achievements, and in some cases, suggested that certain claims in the literature may be unduly optimistic.
Studying interaction methodologies in video retrieval
Authors: Hopfgartner Frank
Published: June 2014

So far, several approaches have been studied to bridge the problem of the Semantic Gap, the bottleneck in image and video retrieval. However, no approach is successful enough to increase retrieval performances significantly. One reason is the lack of understanding the user's interest, a major condition towards adapting results to a user. This is partly due to the lack of appropriate interfaces and the missing knowledge of how to interpret user's actions with these interfaces. In this paper, we propose to study the importance of various implicit indicators of relevance. Furthermore, we propose to investigate how this implicit feedback can be combined with static user profiles towards an adaptive video retrieval model.
Declarative management in Microsoft SQL server
Authors: Guo Hongfei , Jones Dan , Beckmann Jennifer , Seshadri Praveen
Published: June 2014

This paper describes the principles and practice of Declarative Management --- a new approach to the management of database systems. The standard approach to database systems management involves a brittle coupling of interactive operations and procedural scripts. Such ad hoc approach results in incorrect administration, which leads to increased management costs. In the Declarative Management paradigm, a user specifies "what" the desired state is, and the system figures out "how" to get there and stay there. Declarative Management represents a fundamental step towards the goal of a self-managing database system. It also has the potential to significantly lower both administrative error and cost. An initial implementation of Declarative Management has been released with the Microsoft SQL Server 2008 database product, and the paper covers the implementation design as well.
Binary XML storage and query processing in Oracle 11g
Authors: Zhang Ning , Agarwal Nipun , Chandrasekar Sivasankaran , Idicula Sam , Medi Vijay , Petride Sabina , Sthanikam Balasubramanyam
Published: June 2014

Oracle RDBMS has supported XML data management for more than six years since version 9i. Prior to 11g, text-centric XML documents can be stored as-is in a CLOB column and schema-based data-centric documents can be shredded and stored in object-relational (OR) tables mapped from their XML Schema. However, both storage formats have intrinsic limitations---XML/CLOB has unacceptable query and update performance, and XML/OR requires XML schema. To tackle this problem, Oracle 11g introduces a native Binary XML storage format and a complete stack of data management operations. Binary XML was designed to address a wide range of real application problems encountered in XML data management---schema flexibility, amenability to XML indexes, update performance, schema evolution, just to name a few.

In this paper, we introduce the Binary XML storage format based on Oracle SecureFiles System[21]. We propose a lightweight navigational index on top of the storage and an NFA-based navigational algorithm to provide efficient streaming processing. We further optimize query processing by exploiting XML structural and schema information that are collected in database dictionary. We conducted extensive experiments to demonstrate high performance of the native Binary XML in query processing, update, and space consumption.
MAD skills
Authors: Cohen Jeffrey , Dolan Brian , Dunlap Mark , Hellerstein Joseph M. , Welton Caleb
Published: June 2014

As massive data acquisition and storage becomes increasingly affordable, a wide variety of enterprises are employing statisticians to engage in sophisticated data analysis. In this paper we highlight the emerging practice of Magnetic, Agile, Deep (MAD) data analysis as a radical departure from traditional Enterprise Data Warehouses and Business Intelligence. We present our design philosophy, techniques and experience providing MAD analytics for one of the world's largest advertising networks at Fox Audience Network, using the Greenplum parallel database system. We describe database design methodologies that support the agile working style of analysts in these settings. We present dataparallel algorithms for sophisticated statistical techniques, with a focus on
density
methods. Finally, we reflect on database system features that enable agile design and flexible algorithm development using both SQL and MapReduce interfaces over a variety of storage mechanisms.
Kosmix
Authors: Rajaraman Anand
Published: June 2014

Kosmix lies at the intersection of two important trends: topic exploration and the Deep Web. Topic exploration is a new approach to information discovery on the web that satisfies certain use cases not served well by conventional web search. The Deep Web, an inhospitable region for web crawlers, is emerging as a significant information resource. We describe the anatomy of Kosmix, the first general-purpose topic exploration engine to harness the Deep Web using a federated search approach. We focus in particular on the Kosmix approach to query tranformation and caching, which is essential to ensure reasonable performance.
A demonstration of SciDB
Authors: Cudre-Mauroux P. , Kimura H. , Lim K.-T. , Rogers J. , Simakov R. , Soroush E. , Velikhov P. , Wang D. L. , Balazinska M. , Becla J. , DeWitt D. , Heath B. , Maier D. , Madden S. , Patel J. , Stonebraker M. , Zdonik S.
Published: June 2014

In CIDR 2009, we presented a collection of requirements for SciDB, a DBMS that would meet the needs of scientific users. These included a nested-array data model, science-specific operations such as regrid, and support for uncertainty, lineage, and named versions. In this paper, we present an overview of SciDB's key features and outline a demonstration of the first version of SciDB on data and operations from one of our lighthouse users, the Large Synoptic Survey Telescope (LSST).
MOIR/MT
Authors: Liu Kuien , Deng Ke , Ding Zhiming , Li Mingshu , Zhou Xiaofang
Published: June 2014

Floating Car Data (FCD) provides an economic complement to infrastructure-based traffic monitoring systems. Based on our previous MOIR platform [5], we use FCD as the data source for large-scale real-time traffic monitoring. This new function brings a challenge of efficiently handling of streaming data from a very large number of moving objects. Server overload problems can occur when a system fails to process data and queries in real-tme, which can lead to critical issues such as unbounded delay accumulation, lost monitoring accuracy or lack of spontaneity. These problems can be addressed by adopting suitable load dropping decisions. In this work, we demonstrate several load shedding techniques, focusing on decision-making based on data attributes. With the end results being quantified and visualized using real data for a large city, this proof-of-concept system provides a convincing way of validating our ideas.
DIADS
Authors: Borisov Nedyalko , Babu Shivnath , Uttamchandani Sandeep , Routray Ramani , Singh Aameek
Published: June 2014

Many enterprise environments have databases running on network-attached storage infrastructure (referred to as
Storage Area Networks
or
SANs
). Both the database and the SAN are complex subsystems that are managed by separate teams of administrators. As often as not, database administrators have limited understanding of SAN configuration and behavior, and limited visibility into the SAN's run-time performance; and vice versa for the SAN administrators. Diagnosing the cause of performance problems is a challenging exercise in these environments. We propose to remedy the situation through a novel tool, called Diads, for database and SAN problem diagnosis. This demonstration proposal summarizes the technical innovations in Diads: (i) a powerful abstraction called
Annotated Plan Graphs (APGs)
that ties together the execution path of queries in the database and the SAN using low-overhead monitoring data, and (ii) a diagnosis workflow that combines domain-specific knowledge with machine-learning techniques. The scenarios presented in the demonstration are also described.
A testbed for managing dynamic mixed workloads
Authors: Krompass Stefan , Kuno Harumi , Wiener Janet L. , Wilkinson Kevin , Dayal Umeshwar , Kemper Alfons
Published: June 2014

Workload management for operational business intelligence (BI) databases is difficult. Queries vary widely in length and objectives. Resource contention is difficult to predict and to control as dynamically-arriving, long, analyst queries compete for resources with ongoing online-transaction processing (OLTP) queries and batch report queries. Currently, administrators struggle to choose workload management policies and set their thresholds manually. The goal of our project is a software framework to make the management of such mixed workloads easier. Our framework includes a policy controller that tunes workload management policies automatically to meet workload objectives. This demonstration of our system illustrates (1) the difficulty of managing a BI database workload and (2) the benefits of tuning policies automatically and individually for each service class of queries in a workload. In addition, our demonstrator is a useful research tool for understanding how policies and a policy controller adapt as the system state changes under a mixed workload.

In our demo, the participant plays the administrator and tunes the policies for a variety of difficult-to-manage workloads as they execute. These policies include admission control, scheduling, and execution control policies. We visualize the policies, the user objectives, and the load on the system components (CPUs, memory, disks) during execution, which helps the participant see whether objectives are being met and make appropriate policy decisions. At the end of each workload, the participant is given the opportunity to compare how their policies met workload objectives versus policies determined by our automatic policy controller.
DBToaster
Authors: Ahmad Yanif , Koch Christoph
Published: June 2014

We present DBToaster, a novel query compilation framework for producing high performance compiled query executors that incrementally and continuously answer standing aggregate queries using in-memory views. DBToaster targets applications that require efficient main-memory processing of standing queries (
views
) fed by high-volume data streams,
recursively
compiling view maintenance (VM) queries into simple C++ functions for evaluating database updates (
deltas
). While today's VM algorithms consider the impact of single deltas on view queries to produce maintenance queries, we recursively consider deltas of maintenance queries and compile to thoroughly transform queries into code. Recursive compilation successively elides certain scans and joins, and eliminates significant query plan interpreter overheads.

In this demonstration, we walk through our compilation algorithm, and show the significant performance advantages of our compiled executors over other query processors. We are able to demonstrate 1--3 orders of magnitude improvements in processing times for a financial application and a data warehouse loading application, both implemented across a wide range of database systems, including PostgreSQL, HSQLDB, a commercial DBMS 'A', the Stanford STREAM engine, and a commercial stream processor 'B'.
ANGIE
Authors: Preda Nicoleta , Suchanek Fabian M. , Kasneci Gjergji , Neumann Thomas , Ramanath Maya , Weikum Gerhard
Published: June 2014

We present ANGIE, a system that can answer user queries by combining knowledge from a local database with knowledge retrieved from Web services. If a user poses a query that cannot be answered by the local database alone, ANGIE calls the appropriate Web services to retrieve the missing information. This information is integrated seamlessly and transparently into the local database, so that the user can query and browse the knowledge base while appropriate Web services are called automatically in the background.
Comparative evaluation of entity resolution approaches with FEVER
Authors: Köpcke Hanna , Thor Andreas , Rahm Erhard
Published: June 2014

We present FEVER, a new evaluation platform for entity resolution approaches. The modular structure of the FEVER framework supports the incorporation or reconstruction of many previously proposed approaches for entity resolution. A distinctive feature of FEVER is that it not only evaluates traditional measures such as precision and recall but also the effort for configuring (e.g., parameter tuning, training) a good entity resolution approach. FEVER thus strives for a fair comparative evaluation of different approaches by considering both the effectiveness and configuration effort.
SMDM
Authors: Wang Xiaoyuan , Sun Xingzhi , Cao Feng , Ma Li , Kanellos Nick , Zhang Kang , Pan Yue , Yu Yong
Published: June 2014

Motivated by evolving business requirements and novel enterprise applications, we propose and implement the Semantic Master Data Management (SMDM), a semantics-level enhancement to the existing MDM solutions. The SMDM system publishes relational-based master data as virtual RDF store, and injects instantaneous reasoning capabilities into semantic queries. Two kinds of ontologies are introduced to the system, the core MDM ontology and the external imported domain ontology. SMDM enables data linking among multi-domains, implicit relationship discovery, and declarative definition and extension of business policies and entities. Based on these functions, modern companies can customize their applications and services on demand within the MDM hub. In the demonstration, we build the system environment based on IBM's MDM solution, and run the use cases on the master data of an insurance company.
IBM UFO repository
Authors: Gubanov Michael N. , Popa Lucian , Ho Howard , Pirahesh Hamid , Chang Jeng-Yih , Chen Shr-Chang
Published: June 2014

Currently,
WWW
, large enterprises, and desktop users suffer from an inability to efficiently access and manage
differently structured
data. The same data objects (e.g. Product) stored by different databases, repositories, distributed web storage systems,
etc
are named, referenced, and combined internally into schemas or data structures differently. This leads to
structural mismatch
of data that often consists of the same semantic objects (e.g. EBay and Yahoo! online auction offers).
Enabling social networking in ad hoc networks of mobile phones
Authors: Sarigöl Emre , Riva Oriana , Stuedi Patrick , Alonso Gustavo
Published: June 2014

This demonstration presents AdSocial, a software platform supporting social network applications in ad hoc networks. AdSocial targets small-scale scenarios such as friends playing a game on the train or co-workers sharing calendar information. Moreover, AdSocial is specifically designed to run on resource-constrained mobile devices, such as mobile phones. By using a simple and efficient data piggybacking mechanism, AdSocial applications share data by using any of the many existing routing protocols for ad hoc networks and without requiring any modification to the protocols themselves. The goal of this demonstration is to show the functionality that AdSocial can support with a very low overhead in an ad hoc network of 10--15 Nokia N810 handhelds. Conference participants will be able to establish voice-video calls, chat, or play games while moving around thus configuring a mobile and multi-hop ad hoc network.
Keyword querying and ranking in databases
Authors: Chaudhuri Surajit , Das Gautam
Published: June 2014

With the proliferation of data sources exposed through web interfaces to consumers, simple ways of exploring contents of such databases are of increasing importance. Examples include users wishing to search catalogs of homes, cars, cameras, restaurants, and photographs. One approach that has been explored is to allow users to query such databases in the same ways as they explore web documents. Thus, it is desirable to be able to use the paradigm of keyword querying and automated result ranking over contents of databases. However, the rich relationships and schema information present in databases makes a direct adaptation of information retrieval techniques inappropriate. This problem has attracted much attention in research as it presents a rich set of challenges from defining semantics of such querying model to developing algorithms that ensure adequate performance. In this tutorial, we focus on the highlights of research progress in this field.
Scalable delivery of stream query result
Authors: Zhou Yongluan , Salehi Ali , Aberer Karl
Published: June 2014

Continuous queries over data streams typically produce large volumes of result streams. To scale up the system, one should carefully study the problem of delivering the result streams to the end users, which, unfortunately, is often overlooked in existing systems. In this paper, we leverage Distributed Publish/Subscribe System (DPSS), a scalable data dissemination infrastructure, for efficient stream query result delivery. To take advantage of DPSS's multicast-like data dissemination architecture, one has to exploit the common contents among different result streams and maximize the sharing of their delivery. Hence, we propose to merge the user queries into a few representative queries whose results subsume those of the original ones, and disseminate the result streams of these representative queries through the DPSS. To realize this approach, we study the stream query containment theories and propose efficient query grouping and merging algorithms. The proposed approach is non-intrusive and hence can be easily implemented as a middleware to be incorporated into existing stream processing systems. A prototype is developed on top of an open-source stream processing system and results of an extensive performance study on real datasets verify the efficacy of the proposed techniques.
Randomized multi-pass streaming skyline algorithms
Authors: Das Sarma Atish , Lall Ashwin , Nanongkai Danupon , Xu Jun
Published: June 2014

We consider external algorithms for skyline computation without pre-processing. Our goal is to develop an algorithm with a good worst case guarantee while performing well on average. Due to the nature of disks, it is desirable that such algorithms access the input as a stream (even if in multiple passes). Using the tools of randomness, proved to be useful in many applications, we present an efficient multi-pass streaming algorithm, RAND, for skyline computation. As far as we are aware, RAND is the first randomized skyline algorithm in the literature.

RAND is near-optimal for the streaming model, which we prove via a simple lower bound. Additionally, our algorithm is distributable and can handle partially ordered domains on each attribute. Finally, we demonstrate the robustness of RAND via extensive experiments on both real and synthetic datasets. RAND is comparable to the existing algorithms in average case and additionally tolerant to simple modifications of the data, while other algorithms degrade considerably with such variation.
Measure-driven keyword-query expansion
Authors: Sarkas Nikos , Bansal Nilesh , Das Gautam , Koudas Nick
Published: June 2014

User generated content has been fueling an explosion in the amount of available textual data. In this context, it is also common for users to express, either explicitly (through numerical ratings) or implicitly, their views and opinions on products, events, etc. This wealth of textual information necessitates the development of novel searching and data exploration paradigms.

In this paper we propose a new searching model, similar in spirit to faceted search, that enables the progressive refinement of a keyword-query result. However, in contrast to faceted search which utilizes domain-specific and hard-to-extract document attributes, the refinement process is driven by suggesting interesting
expansions
of the original query with additional search terms. Our
query-driven and domain-neutral
approach employs surprising word co-occurrence patterns and (optionally) numerical user ratings in order to identify meaningful top-
k
query expansions and allow one to focus on a particularly interesting subset of the original result set.

The proposed functionality is supported by a framework that is computationally efficient and nimble in terms of storage requirements. Our solution is grounded on Convex Optimization principles that allow us to exploit the pruning opportunities offered by the natural top-
k
formulation of our problem. The performance benefits offered by our solution are verified using both synthetic data and large real data sets comprised of blog posts.