bluuebunny/crossref_metadata_2025_split huggingface dataset:
Distance-based outlier detection
Authors: Orair Gustavo H. , Teixeira Carlos H. C. , Meira Wagner , Wang Ye , Parthasarathy Srinivasan 
Published: June 2014

Detecting outliers in data is an important problem with interesting applications in a myriad of domains ranging from data cleaning to financial fraud detection and from network intrusion detection to clinical diagnosis of diseases. Over the last decade of research, distance-based outlier detection algorithms have emerged as a viable, scalable, parameter-free alternative to the more traditional statistical approaches.
In this paper we assess several distance-based outlier detection approaches and evaluate them. We begin by surveying and examining the design landscape of extant approaches, while identifying key design decisions of such approaches. We then implement an outlier detection framework and conduct a factorial design experiment to understand the pros and cons of various optimizations proposed by us as well as those proposed in the literature, both independently and in conjunction with one another, on a diverse set of real-life datasets. To the best of our knowledge this is the first such study in the literature. The outcome of this study is a family of state of the art distance-based outlier detection algorithms.
Our detailed empirical study supports the following observations. The combination of optimization strategies enables significant efficiency gains. Our factorial design study highlights the important fact that no single optimization or combination of optimizations (factors) always dominates on all types of data. Our study also allows us to characterize when a certain combination of optimizations is likely to prevail and helps provide interesting and useful insights for moving forward in this domain.
iFlow
Authors: McConnell Christopher , Ping Fan , Hwang Jeong-Hyon 
Published: June 2014

We propose to demonstrate iFlow, our replication-based system that supports both fast and reliable processing of data streams over the Internet. iFlow uses a low degree of replication in conjunction with detouring techniques to overcome network outages. iFlow also deploys replicas in a manner that improves performance and availability at the same time, and can cope with varying system conditions by continually migrating replicas.
Based on a live network monitoring application, our demonstration will substantiate the strengths of iFlow. During the demonstration, various visual tools will provide graphical evidence of improvements with regards to availability, performance, and resource usage. To show iFlow's adaptivity, these tools will also allow us to control the demonstration situation including injecting different types of failures.
Event processing
Authors: Etzion Opher 
Published: June 2014

Analysts have marked
Event Processing
as the most growing segment in enterprise computing during years 2008 and 2009, furthermore, this trend is expected to continue. Many of the large and medium software companies (IBM, Oracle, Microsoft, Sybase, Progress Software, Software AG and TIBCO) are now offering event processing products as well as a collection of smaller companies. Other indications for the emerging nature of this area are: extensive coverage by analysts as a separate area, the establishment of a dedicated research community with an annual conference (DEBS), and the establishment of a consortium that includes vendors and academic people as EPTS (Event Processing Technical Society) http://www.ep-ts.com/.

The early event processing commercial products were mostly descendents of research projects rooted in multiple disciplines, some of them are data management disciplines (active databases, data stream management, temporal databases) and some are rooted in other areas (discrete event simulation, distributed computing, formal verification).
The tutorial is intended for a technical audience that is interested in deep dive into understanding event processing. The audience will gain insights about event processing: What it really means? Where does it come from? How does it relate to research concepts (e.g. stream computing) as well as enterprise computing terms (e.g. Business Rules Management Systems)? The audience will also gain insights into the current state of the art, the leading architectures, the basic building blocks of event processing, and the various programming styles exemplified by code examples. Last but not least, the audience will gain insights about the current trends, and the research challenges that exist, this part will be based on the discussions in the Event Processing Dagstuhl seminar that was held in May 2010 http://www.dagstuhl.de/de/programm/kalender/semhp/?semnr=10201. The tutorial slides are available for public viewing on slideshare http://www.slideshare.net/search/slideshow?q=opher+etzion+%2B+vldb2010+tutorial The current generation of event processing products [14] has been preceded by several research projects in the 1990-ies: Rapide in Stanford [15], Infospheres in Cal Tech [2], Apama in Cambridge University [6] and Amit in IBM Haifa Research Lab [1]. In later phase there were some research project that have taken the stream oriented approach such as the Stanford Stream project [3] and Aurora [5].
A collection of start-up companies, many of them descendents of these projects have emerged, some survived, and some did not. From those who survived some were acquired by bigger companies.
Event processing as a research discipline has multiple ancestors. In the database area it is a descendent of work done in active databases [19], temporal databases [12], and data stream management systems [9]. Other ancestors are inference rules, discrete event simulation, and distributed computing (pub/sub).
Fast updates on read-optimized databases using multi-core CPUs
Authors: Krueger Jens , Kim Changkyu , Grund Martin , Satish Nadathur , Schwalb David , Chhugani Jatin , Plattner Hasso , Dubey Pradeep , Zeier Alexander 
Published: June 2014

Read-optimized columnar databases use differential updates to handle writes by maintaining a separate write-optimized delta partition which is periodically merged with the read-optimized and compressed main partition. This merge process introduces significant overheads and unacceptable downtimes in update intensive systems, aspiring to combine transactional and analytical workloads into one system.
In the first part of the paper, we report data analyses of 12 SAP Business Suite customer systems. In the second half, we present an optimized merge process reducing the merge overhead of current systems by a factor of 30. Our linear-time merge algorithm exploits the underlying high compute and bandwidth resources of modern multi-core CPUs with architecture-aware optimizations and efficient parallelization. This enables compressed in-memory column stores to handle the transactional update rate required by enterprise applications, while keeping properties of read-optimized databases for analytic-style queries.
Dense subgraph maintenance under streaming edge weight updates for real-time story identification
Authors: Angel Albert , Sarkas Nikos , Koudas Nick , Srivastava Divesh 
Published: June 2014

Recent years have witnessed an unprecedented proliferation of social media. People around the globe author, every day, millions of blog posts, micro-blog posts, social network status updates, etc. This rich stream of information can be used to identify, on an ongoing basis, emerging stories, and events that capture popular attention. Stories can be identified via groups of tightly-coupled real-world entities, namely the people, locations, products, etc., that are involved in the story. The sheer scale, and rapid evolution of the data involved necessitate highly efficient techniques for identifying important stories at every point of time.
The main challenge in real-time story identification is the maintenance of dense subgraphs (corresponding to groups of tightly-coupled entities) under streaming edge weight updates (resulting from a stream of user-generated content). This is the first work to study the efficient maintenance of dense subgraphs under such streaming edge weight updates. For a wide range of definitions of density, we derive theoretical results regarding the magnitude of change that a single edge weight update can cause. Based on these, we propose a novel algorithm, DynDens, which outperforms adaptations of existing techniques to this setting, and yields meaningful results. Our approach is validated by a thorough experimental evaluation on large-scale real and synthetic datasets.
LogBase
Authors: Vo Hoang Tam , Wang Sheng , Agrawal Divyakant , Chen Gang , Ooi Beng Chin 
Published: June 2014

Numerous applications such as financial transactions (e.g., stock trading) are write-heavy in nature. The shift from reads to writes in web applications has also been accelerating in recent years. Write-ahead-logging is a common approach for providing recovery capability while improving performance in most storage systems. However, the separation of log and application data incurs write overheads observed in write-heavy environments and hence adversely affects the write throughput and recovery time in the system.
In this paper, we introduce LogBase -- a scalable log-structured database system that adopts log-only storage for removing the write bottleneck and supporting fast system recovery. It is designed to be dynamically deployed on commodity clusters to take advantage of elastic scaling property of cloud environments. LogBase provides in-memory multiversion indexes for supporting efficient access to data maintained in the log. LogBase also supports transactions that bundle read and write operations spanning across multiple records. We implemented the proposed system and compared it with HBase and a disk-based log-structured record-oriented system modeled after RAMCloud. The experimental results show that LogBase is able to provide sustained write throughput, efficient data access out of the cache, and effective system recovery.
Data management on the spatial web
Authors: Jensen Christian S. 
Published: June 2014

Due in part to the increasing mobile use of the web and the proliferation of geo-positioning, the web is fast acquiring a significant spatial aspect. Content and users are being augmented with locations that are used increasingly by location-based services. Studies suggest that each week, several billion web queries are issued that have local intent and target spatial web objects. These are points of interest with a web presence, and they thus have locations as well as textual descriptions. This development has given prominence to spatial web data management, an area ripe with new and exciting opportunities and challenges. The research community has embarked on inventing and supporting new query functionality for the spatial web.
Different kinds of spatial web queries return objects that are near a location argument and are relevant to a text argument. To support such queries, it is important to be able to rank objects according to their relevance to a query. And it is important to be able to process the queries with low latency. The talk offers an overview of key aspects of the spatial web. Based on recent results obtained by the speaker and his colleagues, the talk explores new query functionality enabled by the setting. Further, the talk offers insight into the data management techniques capable of supporting such functionality.
Challenges in economic massive content storage and management (MCSAM) in the era of self-organizing, self-expanding and self-linking data clusters
Authors: Şahin Kenan 
Published: June 2014

Rapid spread of social networks, global on-line shopping, post 9/11 security oriented linking of data bases and foremost the global adoption of smart phones/devices, among other phenomena, are transforming data clusters into dynamic and almost uncontrollable entities that have their own local intelligence, clients and objectives. The scale and rapidity of change is such that large scale innovations in content storage and management are urgently needed if the diseconomies of scale and complexity are to be mitigated. The field needs to reinvent itself.
Istanbul, a city that has reinvented itself many times is an excellent venue to engage in such a discussion and for me to offer suggestions and proposals that derive from personal experiences that span academia, start ups, R&D firms and Bell Labs as well my early years spent in Istanbul.
The MADlib analytics library
Authors: Hellerstein Joseph M. , Ré Christoper , Schoppmann Florian , Wang Daisy Zhe , Fratkin Eugene , Gorajek Aleksander , Ng Kee Siong , Welton Caleb , Feng Xixuan , Li Kun , Kumar Arun 
Published: June 2014

MADlib is a free, open-source library of in-database analytic methods. It provides an evolving suite of SQL-based algorithms for machine learning, data mining and statistics that run at scale within a database engine, with no need for data import/export to other tools. The goal is for MADlib to eventually serve a role for scalable database systems that is similar to the CRAN library for R: a community repository of statistical methods, this time written with scale and parallelism in mind.In this paper we introduce the MADlib project, including the background that led to its beginnings, and the motivation for its open-source nature. We provide an overview of the library's architecture and design patterns, and provide a description of various statistical methods in that context. We include performance and speedup results of a core design pattern from one of those methods over the Greenplum parallel DBMS on a modest-sized test cluster. We then report on two initial efforts at incorporating academic research into MADlib, which is one of the project's goals.MADlib is freely available at http://madlib.net, and the project is open for contributions of both new methods, and ports to additional database platforms.
MonetDB/DataCell
Authors: Liarou Erietta , Idreos Stratos , Manegold Stefan , Kersten Martin 
Published: June 2014

In DataCell, we design streaming functionalities in a modern relational database kernel which targets big data analytics. This includes exploitation of both its storage/execution engine and its optimizer infrastructure. We investigate the opportunities and challenges that arise with such a direction and we show that it carries significant advantages for modern applications in need for online analytics such as web logs, network monitoring and scientific data management. The major challenge then becomes the efficient support for specialized stream features, e.g., multi-query processing and incremental window-based processing as well as exploiting standard DBMS functionalities in a streaming environment such as indexing.This demo presents DataCell, an extension of the MonetDB open-source column-store for online analytics. The demo gives users the opportunity to experience the features of DataCell such as processing both stream and persistent data and performing window based processing. The demo provides a visual interface to monitor the critical system components, e.g., how query plans transform from typical DBMS query plans to online query plans, how data flows through the query plans as the streams evolve, how DataCell maintains intermediate results in columnar form to avoid repeated evaluation of the same stream portions, etc. The demo also provides the ability to interactively set the test scenarios and various DataCell knobs.
Understanding and managing cascades on large graphs
Authors: Prakash B. Aditya , Faloutsos Christos 
Published: June 2014

How do contagions spread in population networks? Which group should we market to, for maximizing product penetration? Will a given YouTube video go viral? Who are the best people to vaccinate? What happens when two products compete? The objective of this tutorial is to provide an intuitive and concise overview of most important theoretical results and algorithms to help us understand and manipulate such propagation-style processes on large networks. The tutorial contains three parts: (a) Theoretical results on the behavior of fundamental models; (b) Scalable Algorithms for changing the behavior of these processes e.g., for immunization, marketing etc.; and (c) Empirical Studies of diffusion on blogs and on-line websites like Twitter.The problems we focus on are central in surprisingly diverse areas: from computer science and engineering, epidemiology and public health, product marketing to information dissemination. Our emphasis is on intuition behind each topic, and guidelines for the practitioner.
Interoperability in eHealth systems
Authors: Dogac Asuman 
Published: June 2014

Interoperability in eHealth systems is important for delivering quality healthcare and reducing healthcare costs. Some of the important use cases include coordinating the care of chronic patients by enabling the co-operation of many different eHealth systems such as Electronic Health Record Systems (EHRs), Personal Health Record Systems (PHRs) and wireless medical sensor devices; enabling secondary use of EHRs for clinical research; being able to share life long EHRs among different healthcare providers. Although achieving eHealth interoperability is quite a challenge both because there are competing standards and clinical information itself is very complex, there have been a number of successful industry initiatives such as Integrating the Healthcare Enterprise (IHE) Profiles, as well as large scale deployments such as the National Health Information System of Turkey and the epSOS initiative for sharing Electronic Health Records and ePrescriptions in Europe.
This article briefly describes the subjects discussed in the VLDB 2012 tutorial to provide an overview of the issues in eHealth interoperability describing the key technologies and standards, identifying important use cases and the associated research challenges and also describing some of the large scale deployments. The aim is to foster further interest in this area.
Large scale cohesive subgraphs discovery for social network visual analysis
Authors: Zhao Feng , Tung Anthony K. H. 
Published: June 2014

Graphs are widely used in large scale social network analysis nowadays. Not only analysts need to focus on cohesive subgraphs to study patterns among social actors, but also normal users are interested in discovering what happening in their neighborhood. However, effectively storing large scale social network and efficiently identifying cohesive subgraphs is challenging. In this work we introduce a novel subgraph concept to capture the cohesion in social interactions, and propose an I/O efficient approach to discover cohesive subgraphs.
Besides, we propose an analytic system which allows users to perform intuitive, visual browsing on large scale social networks. Our system stores the network as a social graph in the graph database, retrieves a local cohesive subgraph based on the input keywords, and then hierarchically visualizes the subgraph out on orbital layout, in which more important social actors are located in the center. By summarizing textual interactions between social actors as tag cloud, we provide a way to quickly locate active social communities and their interactions in a unified view.
Lightweight locking for main memory database systems
Authors: Ren Kun , Thomson Alexander , Abadi Daniel J. 
Published: June 2014

Locking is widely used as a concurrency control mechanism in database systems. As more OLTP databases are stored mostly or entirely in memory, transactional throughput is less and less limited by disk IO, and lock managers increasingly become performance bottlenecks.
In this paper, we introduce very lightweight locking (VLL), an alternative approach to pessimistic concurrency control for main-memory database systems that avoids almost all overhead associated with traditional lock manager operations. We also propose a protocol called selective contention analysis (SCA), which enables systems implementing VLL to achieve high transactional throughput under high contention workloads. We implement these protocols both in a traditional single-machine multi-core database server setting and in a distributed database where data is partitioned across many commodity machines in a shared-nothing cluster. Our experiments show that VLL dramatically reduces locking overhead and thereby increases transactional throughput in both settings.
Partitioning and ranking tagged data sources
Authors: Eftekhar Milad , Koudas Nick 
Published: June 2014

Online types of expression in the form of social networks, micro-blogging, blogs and rich content sharing platforms have proliferated in the last few years. Such proliferation contributed to the vast explosion in online data sharing we are experiencing today.
One unique aspect of online data sharing is tags manually inserted by content generators to facilitate content description and discovery (e.g., hashtags in tweets). In this paper we focus on these tags and we study and propose algorithms that make use of tags in order to automatically organize and categorize this vast collection of socially contributed and tagged information. In particular, we take a holistic approach in organizing such tags and we propose algorithms to partition as well as rank this information collection. Our partitioning algorithms aim to segment the entire collection of tags (and the associated content) into a specified number of partitions for specific problem constraints. In contrast our ranking algorithms aim to identify few partitions fast, for suitably defined ranking functions.
We present a detailed experimental study utilizing the full twitter firehose (set of all tweets in the Twitter service) that attests to the practical utility and effectiveness of our overall approach. We also present a detailed qualitative study of our results.
Mining and indexing graphs for supergraph search
Authors: Yuan Dayu , Mitra Prasenjit , Giles C. Lee 
Published: June 2014

We study supergraph search (SPS), that is, given a query graphqand a graph databaseGthat contains a collection of graphs , return graphs that haveqas a supergraph fromG. SPS has broad applications in bioinformatics, cheminformatics and other scientific and commercial fields. Determining whether a graph is a subgraph (or supergraph) of another is an NP-complete problem. Hence, it is intractable to compute SPS for large graph databases. Two separate indexing methods, a "filter + verify"-based method and a "prefix-sharing"-based method, have been studied to efficiently compute SPS. To implement the above two methods, subgraph patterns are mined from the graph database to build an index. Those subgraphs are mined to optimize either the filtering gain or the prefix-sharing gain. However, no single subgraph-mining algorithm considers both gains.This work is the first one to mine subgraphs to optimize both the filtering gain and the prefix-sharing gain while processing SPS queries. First, we show that the subgraph-mining problem is NP-hard. Then, we propose two polynomial-time algorithms to solve the problem with an approximation ratio of 1-1/eand 1/4 respectively. In addition, we construct a lattice-like index, LW-index, to organize the selected subgraph patterns for fast index-lookup. Our experiments show that our approach improves the query processing time for SPS queries by a factor of 3 to 10.