bluuebunny/crossref_metadata_2025 huggingface dataset:
RACE
Authors: Mansour Essam , El-Roby Ahmed , Kalnis Panos , Ahmadia Aron , Aboulnaga Ashraf
Published: June 2014

A wide range of applications, including bioinformatics, time series, and log analysis, depend on the identification of repetitions in very long sequences. The problem of finding maximal pairs subsumes most important types of repetition-finding tasks. Existing solutions require both the input sequence and its index (typically an order of magnitude larger than the input) to fit in memory. Moreover, they are serial algorithms with long execution time. Therefore, they are limited to small datasets, despite the fact that modern applications demand orders of magnitude longer sequences.

In this paper we present RACE, a parallel system for finding maximal pairs in very long sequences. RACE supports parallel execution on stand-alone multicore systems, in addition to scaling to thousands of nodes on clusters or supercomputers. RACE does not require the input or the index to fit in memory; therefore, it supports very long sequences with limited memory. Moreover, it uses a novel array representation that allows for cache-efficient implementation. RACE is particularly suitable for the cloud (e.g., Amazon EC2) because, based on availability, it can scale elastically to more or fewer machines during its execution. Since scaling out introduces overheads, mainly due to load imbalance, we propose a cost model to estimate the expected speedup, based on statistics gathered through sampling. The model allows the user to select the appropriate combination of cloud resources based on the provider's prices and the required deadline. We conducted extensive experimental evaluation with large real datasets and large computing infrastructures. In contrast to existing methods, RACE can handle the entire human genome on a typical desktop computer with 16GB RAM. Moreover, for a problem that takes 10 hours of serial execution, RACE finishes in 28 seconds using 2,048 nodes on an IBM BlueGene/P supercomputer.
Top-K nearest keyword search on large graphs
Authors: Qiao Miao , Qin Lu , Cheng Hong , Yu Jeffrey Xu , Tian Wentao
Published: June 2014

It is quite common for networks emerging nowadays to have labels or textual contents on the nodes. On such networks, we study the problem of top-knearest keyword (k-NK) search. In a networkGmodeled as an undirected graph, each node is attached with zero or more keywords, and each edge is assigned with a weight measuring its length. Given a query nodeqinGand a keyword λ, a k-NK query seeksknodes which contain λ and are nearest toq. k-NK is not only useful as a stand-alone query but also as a building block for tackling complex graph pattern matching problems.

The key to an accurate k-NK result is a precise shortest distance estimation in a graph. Based on the latest distance oracle technique, we build a shortest path tree for a distance oracle and use the tree distance as a more accurate estimation. With such representation, the original k-NK query on a graph can be reduced to answering the query on a set of trees and then assembling the results obtained from the trees. We propose two efficient algorithms to report the exact k-NK result on a tree. One is query time optimized for a scenario when a small number of result nodes are of interest to users. The other handles k-NK queries for an arbitrarily largekefficiently. In obtaining a k-NK result on a graph from that on trees, a global storage technique is proposed to further reduce the index size and the query time. Extensive experimental results conform with our theoretical findings, and demonstrate the effectiveness and efficiency of our k-NK algorithms on large real graphs.
Continuous cloud-scale query optimization and processing
Authors: Bruno Nicolas , Jain Sapna , Zhou Jingren
Published: June 2014

Massive data analysis in cloud-scale data centers plays a crucial role in making critical business decisions. High-level scripting languages free developers from understanding various system trade-offs, but introduce new challenges for query optimization. One key optimization challenge is missing accurate data statistics, typically due to massive data volumes and their distributed nature, complex computation logic, and frequent usage of user-defined functions. In this paper we propose novel techniques to adapt query processing in the Scope system, the cloud-scale computation environment in Microsoft Online Services. We continuously monitor query execution, collect actual runtime statistics, and adapt parallel execution plans as the query executes. We discuss similarities and differences between our approach and alternatives proposed in the context of traditional centralized systems. Experiments on large-scale Scope production clusters show that the proposed techniques systematically solve the challenge of missing/inaccurate data statistics, detect and resolve partition skew and plan structure, and improve query latency by a few folds for real workloads. Although we focus on optimizing high-level languages, the same ideas are also applicable for MapReduce systems.
MillWheel
Authors: Akidau Tyler , Balikov Alex , Bekiroğlu Kaya , Chernyak Slava , Haberman Josh , Lax Reuven , McVeety Sam , Mills Daniel , Nordstrom Paul , Whittle Sam
Published: June 2014

MillWheel is a framework for building low-latency data-processing applications that is widely used at Google. Users specify a directed computation graph and application code for individual nodes, and the system manages persistent state and the continuous flow of records, all within the envelope of the framework's fault-tolerance guarantees.

This paper describes MillWheel's programming model as well as its implementation. The case study of a continuous anomaly detector in use at Google serves to motivate how many of MillWheel's features are used. MillWheel's programming model provides a notion of logical time, making it simple to write time-based aggregations. MillWheel was designed from the outset with fault tolerance and scalability in mind. In practice, we find that MillWheel's unique combination of scalability, fault tolerance, and a versatile programming model lends itself to a wide variety of problems at Google.
Scuba
Authors: Abraham Lior , Allen John , Barykin Oleksandr , Borkar Vinayak , Chopra Bhuwan , Gerea Ciprian , Merl Daniel , Metzler Josh , Reiss David , Subramanian Subbu , Wiener Janet L. , Zed Okay
Published: June 2014

Facebook takes performance monitoring seriously. Performance issues can impact over one billion users so we track thousands of servers, hundreds of PB of daily network traffic, hundreds of daily code changes, and many other metrics. We require latencies of under a minute from events occuring (a client request on a phone, a bug report filed, a code change checked in) to graphs showing those events on developers' monitors.

Scuba is the data management system Facebook uses for most real-time analysis. Scuba is a fast, scalable, distributed, in-memory database built at Facebook. It currently ingests millions of rows (events) per second and expires data at the same rate. Scuba stores data completely in memory on hundreds of servers each with 144 GB RAM. To process each query, Scuba aggregates data from all servers. Scuba processes almost a million queries per day. Scuba is used extensively for interactive, ad hoc, analysis queries that run in under a second over live data. In addition, Scuba is the workhorse behind Facebook's code regression analysis, bug report monitoring, ads revenue monitoring, and performance debugging.
F1
Authors: Shute Jeff , Vingralek Radek , Samwel Bart , Handy Ben , Whipkey Chad , Rollins Eric , Oancea Mircea , Littlefield Kyle , Menestrina David , Ellner Stephan , Cieslewicz John , Rae Ian , Stancescu Traian , Apte Himani
Published: June 2014

F1 is a distributed relational database system built at Google to support the AdWords business. F1 is a hybrid database that combines high availability, the scalability of NoSQL systems like Bigtable, and the consistency and usability of traditional SQL databases. F1 is built on Spanner, which provides synchronous cross-datacenter replication and strong consistency. Synchronous replication implies higher commit latency, but we mitigate that latency by using a hierarchical schema model with structured data types and through smart application design. F1 also includes a fully functional distributed SQL query engine and automatic change tracking and publishing.
DB2 with BLU acceleration
Authors: Raman Vijayshankar , Attaluri Gopi , Barber Ronald , Chainani Naresh , Kalmuk David , KulandaiSamy Vincent , Leenstra Jens , Lightstone Sam , Liu Shaorong , Lohman Guy M. , Malkemus Tim , Mueller Rene , Pandis Ippokratis , Schiefer Berni , Sharpe David , Sidle Richard , Storm Adam , Zhang Liping
Published: June 2014

DB2 with BLU Acceleration deeply integrates innovative new techniques for defining and processing column-organized tables that speed read-mostly Business Intelligence queries by 10 to 50 times and improve compression by 3 to 10 times, compared to traditional row-organized tables, without the complexity of defining indexes or materialized views on those tables. But DB2 BLU is much more than just a column store. Exploiting frequency-based dictionary compression and main-memory query processing technology from the Blink project at IBM Research - Almaden, DB2 BLU performs most SQL operations - predicate application (even range predicates and IN-lists), joins, and grouping - on the compressed values, which can be packed bit-aligned so densely that multiple values fit in a register and can be processed simultaneously via SIMD (single-instruction, multipledata) instructions. Designed and built from the ground up to exploit modern multi-core processors, DB2 BLU's hardware-conscious algorithms are carefully engineered to maximize parallelism by using novel data structures that need little latching, and to minimize data-cache and instruction-cache misses. Though DB2 BLU is optimized for in-memory processing, database size is not limited by the size of main memory. Fine-grained synopses, late materialization, and a new probabilistic buffer pool protocol for scans minimize disk I/Os, while aggressive prefetching reduces I/O stalls. Full integration with DB2 ensures that DB2 with BLU Acceleration benefits from the full functionality and robust utilities of a mature product, while still enjoying order-of-magnitude performance gains from revolutionary technology without even having to change the SQL, and can mix column-organized and row-organized tables in the same tablespace and even within the same query.
Adaptive and big data scale parallel execution in oracle
Authors: Bellamkonda Srikanth , Li Hua-Gang , Jagtap Unmesh , Zhu Yali , Liang Vince , Cruanes Thierry
Published: June 2014

This paper showcases some of the newly introduced parallel execution methods in Oracle RDBMS. These methods provide highly scalable and adaptive evaluation for the most commonly used SQL operations - joins, group-by, rollup/cube, grouping sets, and window functions. The novelty of these techniques is their use of multi-stage parallelization models, accommodation of optimizer mistakes, and the runtime parallelization and data distribution decisions. These parallel plans adapt based on the statistics gathered on the real data at query execution time. We realized enormous performance gains from these adaptive parallelization techniques. The paper also discusses our approach to parallelize queries with operations that are inherently serial. We believe all these techniques will make their way into big data analytics and other massively parallel database systems.
Overview of turn data management platform for digital advertising
Authors: Elmeleegy Hazem , Li Yinan , Qi Yan , Wilmot Peter , Wu Mingxi , Kolay Santanu , Dasdan Ali , Chen Songting
Published: June 2014

This paper gives an overview of Turn Data Management Platform (DMP). We explain the purpose of this type of platforms, and show how it is positioned in the current digital advertising ecosystem. We also provide a detailed description of the key components in Turn DMP. These components cover the functions of (1) data ingestion and integration, (2) data warehousing and analytics, and (3) real-time data activation. For all components, we discuss the main technical and research challenges, as well as the alternative design choices. One of the main goals of this paper is to highlight the central role that data management is playing in shaping this fast growing multi-billion dollars industry.
Context-aware computing
Authors: Chang Edward Y.
Published: June 2014

A 2011 Gartner report [3] describes context-aware computing as a game-changing opportunity for enterprises to improve both productivity and profits. Context-aware computing is about making applications and content more relevant to a user's context, e.g., when and where the user is, thereby improving user experience. For instance, a coupon delivered to a user at a wrong time or at a wrong location is considered a nuisance. On the contrary, receiving a timely, usable coupon before purchasing a merchandise is a treat. Context-aware computing is not a new concept, but the ongoing mobile revolution makes it both necessary and feasible. • Necessary because the mobile phone display is small and information must be delivered with much higher relevance and precision to meet user needs. • Feasible because small, light-weight mobile devices allow users to almost always carry them around, and much can be learned via a phone about its user's habits and states.

Context-aware computing involves first acquiring context and then taking context-dependent actions. For instance, a phone can sense a user's location and turn off its GPS unit to conserve power when the user enters a building, or it can collect EKG signals of a user and trigger an alert if the user's heart beats irregularly. Similarly, a restaurant can send a coupon to a user when that user is queued up in front of a nearby restaurant. The useful context can be divided into three categories: information on the user (knowledge of habits, emotional state, biophysiological conditions), the user's environment (time, location, co-location of others, social interaction), and the user's tasks (transportation mode, engaged tasks, general goals) [4]. Context-aware computing can be applied to benefit applications in many areas including but not limited to information retrieval, facility management, productivity enhancement, in addition to the aforementioned three examples representing power management, health care, and commerce, respectively.
Keeping the TPC relevant!
Authors: Nambiar Raghunath , Poess Meikel
Published: June 2014

The Transaction Processing Performance Council (TPC) is a nonprofit organization founded in 1988 to define transaction processing and database benchmarks. Since then, the TPC has played a crucial role in providing the industry with relevant standards for total system performance, price-performance, and energy-efficiency comparisons. TPC benchmarks are widely used by database researchers and academia. Historically known for database-centric standards, the TPC has developed a benchmark for virtualization and is currently developing a multisource data integration benchmark. The technology landscape is changing at a rapid pace, challenging industry experts and researchers to develop innovative techniques for evaluating, measuring, and characterizing the performance of modern application systems. The Technology Conference series on Performance Evaluation and Benchmarking (TPCTC), introduced in 2009, and the new TPC-Express initiatives are steps taken by the TPC to be relevant in the coming years and beyond.
Toward scalable transaction processing
Authors: Ailamaki Anastasia , Johnson Ryan , Pandis Ippokratis , Tözün Pínar
Published: June 2014

Designing scalable transaction processing systems on modern multicore hardware has been a challenge for almost a decade. The typical characteristics of transaction processing workloads lead to a high degree of unbounded communication on multicores for conventional system designs.

In this tutorial, we initially present a systematic way of eliminating scalability bottlenecks of a transaction processing system, which is based on minimizing the unbounded communication. Then, we show several techniques that apply the presented methodology to minimize logging, locking, latching etc. related bottlenecks of transaction processing systems. In parallel, we demonstrate the internals of the Shore-MT storage manager and how they have evolved over the years in terms of scalability on multicore hardware through such techniques. We also teach how to use Shore-MT with the various design options it offers through its application layer Shore-Kits and Metadata Frontend.
Mobility and social networking
Authors: Mokbel Mohamed F. , Sarwat Mohamed
Published: June 2014

This tutorial presents the state-of-the-art research that lies at the intersection of two hot topics in the data management community: (1) social networking and (2) mobility. In this tutorial, we give an overview of existing research work, systems, and applications related to both social networking and mobility. In addition, we introduce several resources (i.e., datasets, software tools) as well as a list of promising research directions.
When speed has a price
Authors: Simões Gonçalo , Galhardas Helena , Gravano Luis
Published: June 2014

A wealth of information produced by individuals and organizations is expressed in natural language text. This is a problem since text lacks the explicit structure that is necessary to support rich querying and analysis. Information extraction systems are sophisticated software tools to discover structured information in natural language text. Unfortunately, information extraction is a challenging and time-consuming task. In this paper, we address the limitations of state-of-the-art systems for the optimization of information extraction programs, with the objective of producing efficient extraction executions. Our solution relies on exploiting a wide range of optimization opportunities. For efficiency, we consider a wide spectrum of execution plans, including approximate plans whose results differ in their precision and recall. Our optimizer accounts for these characteristics of the competing execution plans, and uses accurate predictors of their extraction time, recall, and precision. We demonstrate the efficiency and effectiveness of our optimizer through a large-scale experimental evaluation over real-world datasets and multiple extraction tasks and approaches.
Design and evaluation of storage organizations for read-optimized main memory databases
Authors: Chasseur Craig , Patel Jignesh M.
Published: June 2014

Existing main memory data processing systems employ a variety of storage organizations and make a number of storage-related design choices. The focus of this paper is on systematically evaluating a number of these key storage design choices for main memory analytical (i.e. read-optimized) database settings. Our evaluation produces a number of key insights: First, it is always beneficial to organize data into self-contained memory blocks rather than large files. Second, both column-stores and row-stores display performance advantages for different types of queries, and for high performance both should be implemented as options for the tuple-storage layout. Third, cache-sensitive B+-tree indices can play a major role in accelerating query performance, especially when used in a block-oriented organization. Finally, compression can also play a role in accelerating query performance depending on data distribution and query selectivity.
Bitlist
Authors: Rao Weixiong , Chen Lei , Hui Pan , Tarkoma Sasu
Published: June 2014

Nowadays Web search engines are experiencing significant performance challenges caused by a huge amount of Web pages and increasingly larger number of Web users. The key issue for addressing these challenges is to design a compact structure which can index Web documents with low space and meanwhile process keyword search very fast. Unfortunately, the current solutions typically separate the space optimization from the search improvement. As a result, such solutions either save space yet with search inefficiency, or allow fast keyword search but with huge space requirement. In this paper, to address the challenges, we propose a novel structure bitlist with both low space requirement and supporting fast keyword search. Specifically, based on a simple and yet very efficient encoding scheme, bitlist uses a single number to encode a set of integer document IDs for low space, and adopts fast bitwise operations for very efficient boolean-based keyword search. Our extensive experimental results on real and synthetic data sets verify that bitlist outperforms the recent proposed solution, inverted list compression [23, 22] by spending 36.71% less space and 61.91% faster processing time, and achieves comparable running time as [8] but with significantly lower space.
RCSI
Authors: Wandelt Sebastian , Starlinger Johannes , Bux Marc , Leser Ulf
Published: June 2014

Until recently, genomics has concentrated on comparing sequences between species. However, due to the sharply falling cost of sequencing technology, studies of populations of individuals of the same species are now feasible and promise advances in areas such as personalized medicine and treatment of genetic diseases. A core operation in such studies is read mapping, i.e., finding all parts of a set of genomes which are within edit distancekto a given query sequence (k-approximate search). To achieve sufficient speed, current algorithms solve this problem only for one to-be-searched genome and compute only approximate solutions, i.e., they miss somek- approximate occurrences.

We present RCSI, Referentially Compressed Search Index, which scales to a thousand genomes and computes the exact answer. It exploits the fact that genomes of different individuals of the same species are highly similar by first compressing the to-be-searched genomes with respect to a reference genome. Given a query, RCSI then searches the reference and all genome-specific individual differences. We propose efficient data structures for representing compressed genomes and present algorithms for scalable compression and similarity search. We evaluate our algorithms on a set of 1092 human genomes, which amount to approx. 3 TB of raw data. RCSI compresses this set by a ratio of 450:1 (26:1 including the search index) and answers similarity queries on a mid-class server in 15 ms on average even for comparably large error thresholds, thereby significantly outperforming other methods. Furthermore, we present a fast and adaptive heuristic for choosing the best reference sequence for referential compression, a problem that was never studied before at this scale.
Approximate MaxRS in spatial databases
Authors: Tao Yufei , Hu Xiaocheng , Choi Dong-Wan , Chung Chin-Wan
Published: June 2014

In the maximizing range sum (MaxRS) problem, given (i) a setPof 2D points each of which is associated with a positive weight, and (ii) a rectanglerof specific extents, we need to decide where to placerin order to maximize the covered weight ofr- that is, the total weight of the data points covered byr. Algorithms solving the problem exactly entail expensive CPU or I/O cost. In practice, exact answers are often not compulsory in a MaxRS application, where slight imprecision can often be comfortably tolerated, provided that approximate answers can be computed considerably faster. Motivated by this, the present paper studies the (1 - ε)-approximate MaxRS problem, which admits the same inputs as MaxRS, but aims instead to return a rectangle whose covered weight is at least (1-ε)m*, wherem* is the optimal covered weight, and ε can be an arbitrarily small constant between 0 and 1. We present fast algorithms that settle this problem with strong theoretical guarantees.
ReqFlex
Authors: Smits Grégory , Pivert Olivier , Girault Thomas
Published: June 2014

In this demonstration we present a complete fuzzy-set-based approach to preference queries that tackles the two main questions raised by the introduction of flexibility and personalization when querying relational databases: i) how to efficiently execute preference queries? and, ii) how to help users define preferences and queries? As an answer to the first question, we propose PostgreSQL_f, a module implemented on top of PostgreSQL to handle fuzzy queries. To answer the second question, we propose ReqFlex an intuitive user interface to the definition of preferences and the construction of fuzzy queries.
NADEEF
Authors: Ebaid Amr , Elmagarmid Ahmed , Ilyas Ihab F. , Ouzzani Mourad , Quiane-Ruiz Jorge-Arnulfo , Tang Nan , Yin Si
Published: June 2014

We present NADEEF, an extensible, generic and easy-to-deploy data cleaning system. NADEEF distinguishes between a programming interface and a core to achieve generality and extensibility. The programming interface allows users to specify data quality rules by writing code that implements predefined classes. These classes uniformly define what is wrong with the data and (possibly) how to fix it. We will demonstrate the following features provided by NADEEF. (1) Heterogeneity: The programming interface can be used to express many types of data quality rules beyond the well known CFDs (FDs), MDs and ETL rules. (2) Interdependency: The core algorithms can interleave multiple types of rules to detect and repair data errors. (3) Deployment and extensibility: Users can easily customize NADEEF by defining new types of rules, or by extending the core. (4) Metadata management and data custodians: We show a live data quality dashboard to effectively involve users in the data cleaning process.
CrowdMiner
Authors: Amsterdamer Yael , Grossman Yael , Milo Tova , Senellart Pierre
Published: June 2014

This demo presents CrowdMiner, a system enabling the mining of interesting data patterns from the crowd. While traditional data mining techniques have been used extensively for finding patterns in classic databases, they are not always suitable for the crowd, mainly because humans tend to remember only simple trends and summaries rather than exact details. To address this, CrowdMiner employs a novel crowd-mining algorithm, designed specifically for this context. The algorithm iteratively chooses appropriate questions to ask the crowd, while aiming to maximize the knowledge gain at each step. We demonstrate CrowdMiner through a Well-Being portal, constructed interactively by mining the crowd, and in particular the conference participants, for common health related practices and trends.
TeRec
Authors: Chen Chen , Yin Hongzhi , Yao Junjie , Cui Bin
Published: June 2014

As social media further integrates into our daily lives, people are increasingly immersed in real-time social streams via services such as Twitter and Weibo. One important observation in these online social platforms is that users' interests and the popularity of topics shift very fast, which poses great challenges on existing recommender systems to provide the right topics at the right time. In this paper, we extend the online ranking technique and propose a temporal recommender system - TeRec. In TeRec, when posting tweets, users can get recommendations of topics (hashtags) according to their real-time interests, they can also generate fast feedbacks according to the recommendations. TeRec provides the browser-based client interface which enables the users to access the real time topic recommendations, and the server side processes and stores the real-time stream data. The experimental study demonstrates the superiority of TeRec in terms of temporal recommendation accuracy.
SkySuite
Authors: Nagendra Mithila , Candan K. Selçuk
Published: June 2014

Efficient processing of skyline queries has been an area of growing interest over both static and stream environments. Most existing static and streaming techniques assume that the skyline query is applied to a single data source. Unfortunately, this is not true in many applications in which, due to the complexity of the schema, the skyline query may involve attributes belonging to multiple data sources. Recently, in the context of static environments, various hybrid skyline-join algorithms have been proposed. However, these algorithms suffer from several drawbacks: they often need to scan the data sources exhaustively in order to obtain the set of skyline-join results; moreover, the pruning techniques employed to eliminate the tuples are largely based on expensive pairwise tuple-to-tuple comparisons. On the other hand, most existing streaming methods focus on single stream skyline analysis, thus rendering these techniques unsuitable for applications that require a real-time "join" operation to be carried out before the skyline query can be answered. Based on these observations, we introduce and propose to demonstrate SkySuite: a framework of skyline-join operators that can be leveraged to efficiently process skyline-join queries over both static and stream environments. Among others, SkySuite includes (1) a novel Skyline-Sensitive Join (SSJ) operator that effectively processes skyline-join queries in static environments, and (2) a Layered Skyline-window-Join (LSJ) operator that incrementally maintains skyline-join results over stream environments.
SmartMonitor
Authors: Kotsakos Dimitrios , Sakkos Panos , Kalogeraki Vana , Gunopulos Dimitirios
Published: June 2014

In this demonstration, we are presenting SmartMonitor, a distributed Structural Health Monitoring (SHM) system consisting of smart devices. Over the last few years, the vast majority of smart devices is equipped with accelerometers that can be utilized towards building SHM systems with hundreds of nodes. We describe a scalable, fault-tolerant communication protocol, that performs best-effort time synchronization of the nodes and is used to implement a decentralized version of the popular peak-picking SHM method. The implemented interactive system can be easily installed in any accelerometer-equipped Android device and the user has a number of options for configuring the system or analyzing the collected data and computed outcomes.
EagleTree
Authors: Dayan Niv , Svendsen Martin Kjær , Bjørling Matias , Bonnet Philippe , Bouganim Luc
Published: June 2014

Solid State Drives (SSDs) are a moving target for system designers: they are black boxes, their internals are undocumented, and their performance characteristics vary across models. There is no appropriate analytical model and experimenting with commercial SSDs is cumbersome, as it requires a careful experimental methodology to ensure repeatability. Worse, performance results obtained on a given SSD cannot be generalized. Overall, it is impossible to explore how a given algorithm, say a hash join or LSM-tree insertions, leverages the intrinsic parallelism of a modern SSD, or how a slight change in the internals of an SSD would impact its overall performance. In this paper, we propose a new SSD simulation framework, named EagleTree, which addresses these problems, and enables a principled study of SSD-Based algorithms. The demonstration scenario illustrates the design space for algorithms based on an SSD-based IO stack, and shows how researchers and practitioners can use EagleTree to perform tractable explorations of this complex design space.
PROPOLIS
Authors: Deutch Daniel , Moskovitch Yuval , Tannen Val
Published: June 2014

We consider in this demonstration the (static) analysis of data-centric process-based applications, namely applications that depend on an underlying database and whose control is guided by a finite state transition system. We observe that analysts of such applications often want to do more than analyze a specific instance of the application's process control and database. In particular they want to interactively test and explore the effect on analysis results of different hypothetical modifications applied to the application's transition system and to the underlying database. To that end, we propose a demonstration of PROPOLIS, a system for PROvisioned PrOcess anaLysIS, namely analysis of data-centric processes under hypothetical modification scenarios. Our solution is based on the notion of a provisioned expression (which in turn is based on the notion of data provenance), namely an expression that captures, in a compact way, the analysis result with respect to all possible combinations of scenarios, and allows for their exploration at interactive speed. We will demonstrate PROPOLIS in the context of an online shopping application, letting participants play the role of analysts.
Flexible query processor on FPGAs
Authors: Najafi Mohammadreza , Sadoghi Mohammad , Jacobsen Hans-Arno
Published: June 2014

In this work, we demonstrate Flexible Query Processor (FQP), an online reconfigurable event stream query processor. FQP is an FPGA-based query processor that supports select, project and join queries over event streams at line rate. While processing incoming events, FQP can accept new query expressions, a key distinguishing characteristic from related approaches employing FPGAs for acceleration. Our solution aims to address performance limitations experienced with general purpose processors needing to operate at line rate and lack of on the fly reconfigurability with custom designed hardware solutions on FPGAs.
Mastro studio
Authors: Civili Cristina , Console Marco , De Giacomo Giuseppe , Lembo Domenico , Lenzerini Maurizio , Lepore Lorenzo , Mancini Riccardo , Poggi Antonella , Rosati Riccardo , Ruzzi Marco , Santarelli Valerio , Savo Domenico Fabio
Published: June 2014

Ontology-based data access (OBDA) is a novel paradigm for accessing large data repositories through an ontology, that is a formal description of a domain of interest. Supporting the management of OBDA applications poses new challenges, as it requires to provide effective tools for (i) allowing both expert and non-expert users to analyze the OBDA specification, (ii) collaboratively documenting the ontology, (iii) exploiting OBDA services, such as query answering and automated reasoning over ontologies, e.g., to support data quality check, and (iv) tuning the OBDA application towards optimized performances. To fulfill these challenges, we have built a novel system, called MASTRO STUDIO, based on a tool for automated reasoning over ontologies, enhanced with a suite of tools and optimization facilities for managing OBDA applications. To show the effectiveness of MASTRO STUDIO, we demonstrate its usage in one OBDA application developed in collaboration with the Italian Ministry of Economy and Finance.
EvenTweet
Authors: Abdelhaq Hamed , Sengstock Christian , Gertz Michael
Published: June 2014

Microblogging services such as Twitter, Facebook, and Foursquare have become major sources for information about real-world events. Most approaches that aim at extracting event information from such sources typically use the temporal context of messages. However, exploiting the location information of georeferenced messages, too, is important to detect localized events, such as public events or emergency situations. Users posting messages that are close to the location of an event serve as human sensors to describe an event. In this demonstration, we present a novel framework to detect localized events in real-time from a Twitter stream and to track the evolution of such events over time. For this, spatio-temporal characteristics of keywords are continuously extracted to identify meaningful candidates for event descriptions. Then, localized event information is extracted by clustering keywords according to their spatial similarity. To determine the most important events in a (recent) time frame, we introduce a scoring scheme for events. We demonstrate the functionality of our system, called Even-Tweet, using a stream of tweets from Europe during the 2012 UEFA European Football Championship.
eSkyline
Authors: Bothe Suvarna , Karras Panagiotis , Vlachou Akrivi
Published: June 2014

The advent of cloud computing redefines the traditional query processing paradigm. Whereas computational overhead and memory constraints become less prohibitive, data privacy, security, and confidentiality concerns become top priorities. In particular, as data owners outsource the management of their data to service providers, query processing over such data has more resources to tap into, yet the data oftentimes has to be encrypted so as to prevent unauthorized access. The challenge that arises in such a setting is to devise an encryption scheme that still allows for query results to be efficiently computed using the encrypted data values. An important type of query that raises unconventional requirements in terms of the operator that has to be evaluated is the skyline query, which returns a set of objects in a dataset whose values are not dominated by any other object therein. In this demonstration, we present eSkyline, a prototype system and query interface that enables the processing of skyline queries over encrypted data, even without preserving the order on each attribute as order-preserving encryption would do. Our system comprises of an encryption scheme that facilitates the evaluation of domination relationships, hence allows for state-of-the-art skyline processing algorithms to be used. The actual data values are reconstructed only at the client side, where the encryption key is known. Our demo visualizes the details of the encryption scheme, allows a user to interact with a server, and showcases the efficiency of computing skyline queries and decrypting the results.
Ringtail
Authors: Antenucci Dolan , Li Erdong , Liu Shaobo , Zhang Bochun , Cafarella Michael J. , Ré Christopher
Published: June 2014

Social media nowcasting--using online user activity to describe real-world phenomena--is an active area of research to supplement more traditional and costly data collection methods such as phone surveys. Given the potential impact of such research, we would expect general-purpose nowcasting systems to quickly become a standard tool among noncomputer scientists, yet it has largely remained a research topic. We believe a major obstacle to widespread adoption is the nowcasting feature selection problem. Typical nowcasting systems require the user to choose a handful of social media objects from a pool of billions of potential candidates, which can be a time-consuming and error-prone process.

We have built RINGTAIL, a nowcasting system that helps the user by automatically suggesting high-quality signals. We demonstrate that RINGTALL can make nowcasting easier by suggesting relevant features for a range of topics. The user provides just a short topic query (e.g., unemployment) and a small conventional dataset in order for RINGTALL to quickly return a usable predictive nowcasting model.
IPS
Authors: Xie Min , Lakshmanan Laks V. S. , Wood Peter T.
Published: June 2014

When planning a trip, one essential task is to find a set of Places-of-Interest (POIs) which can be visited during the trip. Using existing travel guides or websites such as Lonely Planet and TripAdvisor, the user has to either manually work out a desirable set of POIs or take pre-configured travel packages; the former can be time consuming while the latter lacks flexibility. In this demonstration, we propose an Interactive Package configuration System (IPS), which visualizes different candidate packages on a map, and enables users to configure a travel package through simple interactions, i.e., comparing packages and fixing/removing POIs from a package. Compared with existing trip planning systems, we believe IPS strikes the right balance between flexibility and manual effort.
DiAl
Authors: Santos Ivo , Tilly Marcel , Chandramouli Badrish , Goldstein Jonathan
Published: June 2014

Connected devices are expected to grow to 50 billion in 2020. Through our industrial partners and their use cases, we validated the importance of inflight data processing to produce results with low latency, in particular local and global data analytics capabilities. In order to cope with the scalability challenges posed by distributed streaming analytics scenarios, we propose two new technologies: (1) JStreams, a low footprint and efficient JavaScript complex event processing engine supporting local analytics on heterogeneous devices and (2) DiAlM, a distributed analytics management service that leverages cloud-edge evolving topologies. In the demonstration, based on a real manufacturing use case, we walk through a situation where operators supervise manufacturing equipment through global analytics, and drill down into alarm cases on the factory floor by locally inspecting the data generated by the manufacturing equipment.
Why it is time for a HyPE
Authors: Breß Sebastian , Saake Gunter
Published: June 2014

GPU acceleration is a promising approach to speed up query processing of database systems by using low cost graphic processors as coprocessors. Two major trends have emerged in this area: (1) The development of frameworks for scheduling tasks in heterogeneous CPU/GPU platforms, which is mainly in the context of coprocessing for applications and does not consider specifics of database-query processing and optimization. (2) The acceleration of database operations using efficient GPU algorithms, which typically cannot be applied easily on other database systems, because of their analytical-algorithm-specific cost models. One major challenge is how to combine traditional database query processing with GPU coprocessing techniques and efficient database operation scheduling in a GPU-aware query optimizer. In this thesis, we develop a hybrid query processing engine, which extends the traditional physical optimization process to generate hybrid query plans and to perform a cost-based optimization in a way that the advantages of CPUs and GPUs are combined. Furthermore, we aim at a portable solution between different GPU-accelerated database management systems to maximize applicability. Preliminary results indicate great potential.
Realtime analysis of information diffusion in social media
Authors: Taxidou Io , Fischer Peter
Published: June 2014

The goal of this thesis is to investigate real-time analysis methods on social media with a focus on information diffusion. From a conceptual point of view, we are interested both in the structural, sociological and temporal aspects of information diffusion in social media with a twist on the real time factor of what is happening right now. From a technical side, the sheer size of current social media services (100's of millions of users) and the large amount of data produced by these users renders conventional approaches for these costly analyses impossible. For that, we need to go beyond the state-of-the-art infrastructure for data-intensive computation. Our high level goal is to investigate how information diffuses in real time on the underlying social network and the role of different users in the propagation process. We plan to implement these analyses with full and partially missing datasets and compare the cost and quality of both approaches.
Efficient querying of inconsistent databases with binary integer programming
Authors: Kolaitis Phokion G. , Pema Enela , Tan Wang-Chiew
Published: June 2014

An inconsistent database is a database that violates one or more integrity constraints. A typical approach for answering a query over an inconsistent database is to first clean the inconsistent database by transforming it to a consistent one and then apply the query to the consistent database. An alternative and more principled approach, known as consistent query answering, derives the answers to a query over an inconsistent database without changing the database, but by taking into account all possible repairs of the database.

In this paper, we study the problem of consistent query answering over inconsistent databases for the class for conjunctive queries under primary key constraints. We develop a system, called EQUIP, that represents a fundamental departure from existing approaches for computing the consistent answers to queries in this class. At the heart of EQUIP is a technique, based on Binary Integer Programming (BIP), that repeatedly searches for repairs to eliminate candidate consistent answers until no further such candidates can be eliminated. We establish rigorously the correctness of the algorithms behind EQUIP and carry out an extensive experimental investigation that validates the effectiveness of our approach. Specifically, EQUIP exhibits good and stable performance on conjunctive queries under primary key constraints, it significantly outperforms existing systems for computing the consistent answers of such queries in the case in which the consistent answers are not first-order rewritable, and it scales well.
Discovering linkage points over web data
Authors: Hassanzadeh Oktie , Pu Ken Q. , Yeganeh Soheil Hassas , Miller Renée J. , Popa Lucian , Hernández Mauricio A. , Ho Howard
Published: June 2014

A basic step in integration is the identification of linkage points, i.e., finding attributes that are shared (or related) between data sources, and that can be used to match records or entities across sources. This is usually performed using a match operator, that associates attributes of one database to another. However, the massive growth in the amount and variety of unstructured and semi-structured data on the Web has created new challenges for this task. Such data sources often do not have a fixed pre-defined schema and contain large numbers of diverse attributes. Furthermore, the end goal is not schema alignment as these schemas may be too heterogeneous (and dynamic) to meaningfully align. Rather, the goal is to align any overlapping data shared by these sources. We will show that even attributes with different meanings (that would not qualify as schema matches) can sometimes be useful in aligning data. The solution we propose in this paper replaces the basic schema-matching step with a more complex instance-based schema analysis and linkage discovery. We present a framework consisting of a library of efficient lexical analyzers and similarity functions, and a set of search algorithms for effective and efficient identification of linkage points over Web data. We experimentally evaluate the effectiveness of our proposed algorithms in real-world integration scenarios in several domains.
IS-Label
Authors: Fu Ada Wai-Chee , Wu Huanhuan , Cheng James , Wong Raymond Chi-Wing
Published: June 2014

We study the problem of computing shortest path or distance between two query vertices in a graph, which has numerous important applications. Quite a number of indexes have been proposed to answer such distance queries. However, all of these indexes can only process graphs of size barely up to 1 million vertices, which is rather small in view of many of the fast-growing real-world graphs today such as social networks and Web graphs. We propose an efficient index, which is a novel labeling scheme based on the independent set of a graph. We show that our method can handle graphs of size orders of magnitude larger than existing indexes.
CorrectDB
Authors: Bajaj Sumeet , Sion Radu
Published: June 2014

Clients of outsourced databases need Query Authentication (QA) guaranteeing the integrity (correctness and completeness), and authenticity of the query results returned by potentially compromised providers.

Existing results provide QA assurances for a limited class of queries by deploying several software cryptographic constructs. Here, we show that, to achieve QA, however, it is significantly cheaper and more practical to deploy server-hosted, tamper-proof co-processors, despite their higher acquisition costs. Further, this provides the ability to handle arbitrary queries.

To reach this insight, we extensively survey existing QA work and identify interdependencies and efficiency relationships. We then introduce CorrectDB, a new DBMS with full QA assurances, leveraging server-hosted, tamper-proof, trusted hardware in close proximity to the outsourced data.