bluuebunny/crossref_metadata_2025 huggingface dataset:
Performance and scalability of indexed subgraph query processing methods
Authors: Katsarou Foteini , Ntarmos Nikos , Triantafillou Peter
Published: September 2015

Graph data management systems have become very popular as graphs are the natural data model for many applications. One of the main problems addressed by these systems is subgraph query processing; i.e., given a query graph, return all graphs that contain the query. The naive method for processing such queries is to perform a subgraph isomorphism test against each graph in the dataset. This obviously does not scale, as subgraph isomorphism is NP-Complete. Thus, many indexing methods have been proposed to reduce the number of candidate graphs that have to underpass the subgraph isomorphism test. In this paper, we identify a set of key factors-parameters, that influence the performance of related methods: namely, the number of nodes per graph, the graph density, the number of distinct labels, the number of graphs in the dataset, and the query graph size. We then conduct comprehensive and systematic experiments that analyze the sensitivity of the various methods on the values of the key parameters. Our aims are twofold: first to derive conclusions about the algorithms' relative performance, and, second, to stress-test all algorithms, deriving insights as to their scalability, and highlight how both performance and scalability depend on the above factors. We choose six well-established indexing methods, namely Grapes, CT-Index, GraphGrepSX, gIndex, Tree+Δ, and gCode, as representative approaches of the overall design space, including the most recent and best performing methods. We report on their index construction time and index size, and on query processing performance in terms of time and false positive ratio. We employ both real and synthetic datasets. Specifically, four real datasets of different characteristics are used: AIDS, PDBS, PCM, and PPI. In addition, we generate a large number of synthetic graph datasets, empowering us to systematically study the algorithms' performance and scalability versus the aforementioned key parameters.
Distributed architecture of Oracle database in-memory
Authors: Mukherjee Niloy , Chavan Shasank , Colgan Maria , Das Dinesh , Gleeson Mike , Hase Sanket , Holloway Allison , Jin Hui , Kamp Jesse , Kulkarni Kartik , Lahiri Tirthankar , Loaiza Juan , Macnaughton Neil , Marwah Vineet , Mullick Atrayee , Witkowski Andy , Yan Jiaqi , Zait Mohamed
Published: September 2015

Over the last few years, the information technology industry has witnessed revolutions in multiple dimensions. Increasing ubiquitous sources of data have posed two connected challenges to data management solutions -- processing unprecedented volumes of data, and providing ad-hoc real-time analysis in mainstream production data stores without compromising regular transactional workload performance. In parallel, computer hardware systems are scaling out elastically, scaling up in the number of processors and cores, and increasing main memory capacity extensively. The data processing challenges combined with the rapid advancement of hardware systems has necessitated the evolution of a new breed of main-memory databases optimized for mixed OLTAP environments and designed to scale.

The Oracle RDBMS In-memory Option (DBIM) is an industry-first distributed dual format architecture that allows a database object to be stored in columnar format in main memory highly optimized to break performance barriers in analytic query workloads, simultaneously maintaining transactional consistency with the corresponding OLTP optimized row-major format persisted in storage and accessed through database buffer cache. In this paper, we present the distributed, highly-available, and fault-tolerant architecture of the Oracle DBIM that enables the RDBMS to transparently scale out in a database cluster, both in terms of memory capacity and query processing throughput. We believe that the architecture is unique among all mainstream in-memory databases. It allows complete application-transparent, extremely scalable and automated distribution of Oracle RDBMS objects in-memory across a cluster, as well as across multiple NUMA nodes within a single server. It seamlessly provides distribution awareness to the Oracle SQL execution framework through affinitized fault-tolerant parallel execution within and across servers without explicit optimizer plan changes or query rewrites.
JetScope
Authors: Boutin Eric , Brett Paul , Chen Xiaoyu , Ekanayake Jaliya , Guan Tao , Korsun Anna , Yin Zhicheng , Zhang Nan , Zhou Jingren
Published: September 2015

Interactive, reliable, and rich data analytics at cloud scale is a key capability to support low latency data exploration and experimentation over terabytes of data for a wide range of business scenarios. Besides the challenges in massive scalability and low latency distributed query processing, it is imperative to achieve all these requirements with effective fault tolerance and efficient recovery, as failures and fluctuations are the norm in such a distributed environment.

We present a cloud scale interactive query processing system, called JetScope, developed at Microsoft. The system has a SQL-like declarative scripting language and delivers massive scalability and high performance through advanced optimizations. In order to achieve low latency, the system leverages various access methods, optimizes delivering first rows, and maximizes network and scheduling efficiency. The system also provides a fine-grained fault tolerance mechanism which is able to efficiently detect and mitigate failures without significantly impacting the query latency and user experience. JetScope has been deployed to hundreds of servers in production at Microsoft, serving a few million queries every day.
Optimization of common table expressions in MPP database systems
Authors: El-Helw Amr , Raghavan Venkatesh , Soliman Mohamed A. , Caragea George , Gu Zhongxian , Petropoulos Michalis
Published: September 2015

Big Data analytics often include complex queries with similar or identical expressions, usually referred to as Common Table Expressions (CTEs). CTEs may be explicitly defined by users to simplify query formulations, or implicitly included in queries generated by business intelligence tools, financial applications and decision support systems. In Massively Parallel Processing (MPP) database systems, CTEs pose new challenges due to the distributed nature of query processing, the overwhelming volume of underlying data and the scalability criteria that systems are required to meet. In these settings, the effective optimization and efficient execution of CTEs are crucial for the timely processing of analytical queries over Big Data. In this paper, we present a comprehensive framework for the representation, optimization and execution of CTEs in the context ofOrca-- Pivotal's query optimizer for Big Data. We demonstrate experimentally the benefits of our techniques using industry standard decision support benchmark.
FIT to monitor feed quality
Authors: Dasu Tamraparni , Shkapenyuk Vladislav , Srivastava Divesh , Swayne Deborah F.
Published: September 2015

While there has been significant focus on collecting and managing data feeds, it is only now that attention is turning to their quality. In this paper, we propose a principled approach to online data quality monitoring in a dynamic feed environment. Our goal is to alert quickly when feed behavior deviates from expectations.

We make contributions in two distinct directions. First, we propose novel enhancements to permit a publish-subscribe approach to incorporate data quality modules into the DFMS architecture. Second, we propose novel temporal extensions to standard statistical techniques to adapt them to online feed monitoring for outlier detection and alert generation atmultiple scalesalong three dimensions: aggregation at multiple time intervals to detect at varying levels of sensitivity; multiple lengths of data history for varying the speed at which models adapt to change; and multiple levels of monitoring delay to address lagged data arrival.

FIT, or Feed Inspection Tool, is the result of a successful implementation of our approach. We present several case studies outlining the effective deployment of FIT in real applications along with user testimonials.
Efficient evaluation of object-centric exploration queries for visualization
Authors: Wu You , Harb Boulos , Yang Jun , Yu Cong
Published: September 2015

The most effective way to explore data is through visualizing the results of exploration queries. For example, an exploration query could be an aggregate of some measures over time intervals, and a pattern or abnormality can be discovered through a time series plot of the query results. In this paper, we examine a special kind of exploration query, namely object-centric exploration query. Common examples include claims made about athletes in sports databases, such as "it is newsworthy that LeBron James has scored 35 or more points in nine consecutive games."

We focus on one common type of visualization, i.e., 2d scatter plot with heatmap. Namely, we consider exploration queries whose results can be plotted on a two-dimensional space, possibly with colors indicating object densities in regions. While we model results as pairs of numbers, the types of the queries are limited only by the users' imagination. In the LeBron James example above, the two dimensions are minimum points scored per game and number of consecutive games, respectively. It is easy to find other equally interesting dimensions, such as minimum rebounds per game or number of playoff games.

We formalize this problem and propose an efficient, interactive-speed algorithm that takes a user-provided exploration query (which can be a blackbox function) and produces an approximate visualization that preserves the two most important visual properties: the outliers and the overall distribution of all result points.
Query optimization in Oracle 12c database in-memory
Authors: Das Dinesh , Yan Jiaqi , Zait Mohamed , Valluri Satyanarayana R. , Vyas Nirav , Krishnamachari Ramarajan , Gaharwar Prashant , Kamp Jesse , Mukherjee Niloy
Published: September 2015

Traditional on-disk row major tables have been the dominant storage mechanism in relational databases for decades. Over the last decade, however, with explosive growth in data volume and demand for faster analytics, has come the recognition that a different data representation is needed. There is widespread agreement that in-memory column-oriented databases are best suited to meet the realities of this new world.

Oracle 12c Database In-memory, the industry's first dual-format database, allows existing row major on-disk tables to have complementary in-memory columnar representations. The new storage format brings new data processing techniques and query execution algorithms and thus new challenges for the query optimizer. Execution plans that are optimal for one format may be sub-optimal for the other.

In this paper, we describe the changes made in the query optimizer to generate execution plans optimized for the specific format -- row major or columnar -- that will be scanned during query execution. With enhancements in several areas -- statistics, cost model, query transformation, access path and join optimization, parallelism, and cluster-awareness -- the query optimizer plays a significant role in unlocking the full promise and performance of Oracle Database In-Memory.
Live programming in the LogicBlox system
Authors: Green Todd J. , Olteanu Dan , Washburn Geoffrey
Published: September 2015

The emerging category of self-service enterprise applications motivates support for "live programming" in the database, where the user's iterative data exploration triggers changes to installed application code and its output in real time.

This paper discusses the technical challenges in supporting live programming in the database and presents the solution implemented in the LogicBlox commercial system. The workhorse architectural component is a "meta-engine" that incrementally maintains metadata representing application code, guides its compilation into an internal representation in the database kernel, and orchestrates maintenance of materialized views based on those changes. Our approach mirrors LogicBlox's declarative programming model and describes the maintenance of application code using declarative meta-rules; the meta-engine is essentially a "bootstrap" version of the database engine proper.

Beyond live programming, the meta-engine turns out effective for a range of static analysis and optimization tasks. Outside of the database context, we speculate that our design may even provide a novel means of building incremental compilers for general-purpose programming languages.
The dataflow model
Authors: Akidau Tyler , Bradshaw Robert , Chambers Craig , Chernyak Slava , Fernández-Moctezuma Rafael J. , Lax Reuven , McVeety Sam , Mills Daniel , Perry Frances , Schmidt Eric , Whittle Sam
Published: September 2015

Unbounded, unordered, global-scale datasets are increasingly common in day-to-day business (e.g. Web logs, mobile usage statistics, and sensor networks). At the same time, consumers of these datasets have evolved sophisticated requirements, such as event-time ordering and windowing by features of the data themselves, in addition to an insatiable hunger for faster answers. Meanwhile, practicality dictates that one can never fully optimize along all dimensions of correctness, latency, and cost for these types of input. As a result, data processing practitioners are left with the quandary of how to reconcile the tensions between these seemingly competing propositions, often resulting in disparate implementations and systems.

We propose that a fundamental shift of approach is necessary to deal with these evolved requirements in modern data processing. We as a field must stop trying to groom unbounded datasets into finite pools of information that eventually become complete, and instead live and breathe under the assumption that we will never know if or when we have seen all of our data, only that new data will arrive, old data may be retracted, and the only way to make this problem tractable is via principled abstractions that allow the practitioner the choice of appropriate tradeoffs along the axes of interest: correctness, latency, and cost.

In this paper, we present one such approach, the Dataflow Model, along with a detailed examination of the semantics it enables, an overview of the core principles that guided its design, and a validation of the model itself via the real-world experiences that led to its development.
A topic-based reviewer assignment system
Authors: Kou Ngai Meng , U Leong Hou , Mamoulis Nikos , Li Yuhong , Li Ye , Gong Zhiguo
Published: September 2015

Peer reviewing is a widely accepted mechanism for assessing the quality of submitted articles to scientific conferences or journals. Conference management systems (CMS) are used by conference organizers to invite appropriate reviewers and assign them to submitted papers. Typical CMS rely on paper bids entered by the reviewers and apply simple matching algorithms to compute the paper assignment. In this paper, we demonstrate our Reviewer Assignment System (RAS), which has advanced features compared to broadly used CMSs. First, RAS automatically extracts the profiles of reviewers and submissions in the form of topic vectors. These profiles can be used to automatically assign reviewers to papers without relying on a bidding process, which can be tedious and error-prone. Second, besides supporting classic assignment models (e.g., stable marriage and optimal assignment), RAS includes a recently published assignment model by our research group, which maximizes, for each paper, the coverage of its topics by the profiles of its reviewers. The features of the demonstration include (1) automatic extraction of paper and reviewer profiles, (2) assignment computation by different models, and (3) visualization of the results by different models, in order to assess their effectiveness.
Data profiling with metanome
Authors: Papenbrock Thorsten , Bergmann Tanja , Finke Moritz , Zwiener Jakob , Naumann Felix
Published: September 2015

Data profiling is the discipline of discovering metadata about given datasets. The metadata itself serve a variety of use cases, such as data integration, data cleansing, or query optimization. Due to the importance of data profiling in practice, many tools have emerged that support data scientists and IT professionals in this task. These tools provide good support for profiling statistics that are easy to compute, but they are usually lacking automatic and efficient discovery of complex statistics, such as inclusion dependencies, unique column combinations, or functional dependencies.

We present Metanome, an extensible profiling platform that incorporates many state-of-the-art profiling algorithms. While Metanome is able to calculate simple profiling statistics in relational data, its focus lies on the automatic
discovery of complex metadata.
Metanome's goal is to provide novel profiling algorithms from research, perform comparative evaluations, and to support developers in building and testing new algorithms. In addition, Metanome is able to rank profiling results according to various metrics and to visualize the, at times, large metadata sets.
Demonstration of Santoku
Authors: Kumar Arun , Jalal Mona , Yan Boqun , Naughton Jeffrey , Patel Jignesh M.
Published: September 2015

Advanced analytics is a booming area in the data management industry and a hot research topic. Almost all toolkits that implement machine learning (ML) algorithms assume that the input is a single table, but most relational datasets are not stored as single tables due to normalization. Thus, analysts often join tables to obtain a denormalized table. Also, analysts typically ignore any functional dependencies among features because ML toolkits do not support them. In both cases, time is wasted in learning over data with redundancy. We demonstrate
Santoku
, a toolkit to help analysts improve the performance of ML over normalized data. Santoku applies the idea of
factorized learning
and automatically decides whether to denormalize or push ML computations through joins. Santoku also exploits database dependencies to provide automatic insights that could help analysts with exploratory feature selection. It is usable as a library in R, which is a popular environment for advanced analytics. We demonstrate the benefits of Santoku in improving ML performance and helping analysts with feature selection.
Provenance for SQL through abstract interpretation
Authors: Müller Tobias , Grust Torsten
Published: September 2015

We demonstrate the

derivation of fine-grained
where
- and
why
-provenance for a rich dialect of SQL

that includes recursion, (correlated) subqueries, windows, grouping/aggregation, and the RDBMS's library of built-in functions. The approach relies on ideas that originate in the programming language community---program slicing and abstract interpretation, in particular. A two-stage process first records a query's control flow decisions and locations of data access before it derives provenance without consultation of the actual data values (rendering the method largely "value-less"). We will bring an interactive demonstrator that uses this provenance information to make input/output dependencies in real-world SQL queries tangible.
Reformulation-based query answering in RDF
Authors: Bursztyn Damian , Goasdoué François , Manolescu Ioana
Published: September 2015

Answering queries over Semantic Web data, i.e., RDF graphs, must account for both
explicit
data and
implicit
data, entailed by the explicit data and the
semantic constraints
holding on them. Two main query answering techniques have been devised, namely
Saturation
-based (S
at
) which precomputes and adds to the graph all implicit information, and
Reformulation
-based (R
ef
) which reformulates the query based on the graph constraints, so that evaluating the reformulated query directly against the explicit data (i.e., without considering the constraints) produces the query answer.

While S
at
is well known, R
ef
has received less attention so far. In particular, reformulated queries often perform poorly if the query is complex. Our demonstration showcases a large set of R
ef
techniques, including but not limited to one we proposed recently. The audience will be able to 1: test them against different datasets, constraints and queries, as well as different well-established systems, 2: analyze and understand the performance challenges they raise, and 3: alter the scenarios to visualize the impact on performance. In particular, we show how a
cost-based
R
ef
approach
allows avoiding reformulation performance pitfalls.
QOCO
Authors: Bergman Moria , Milo Tova , Novgorodov Slava , Tan Wang-Chiew
Published: September 2015

As key decisions are often made based on information contained in a database, it is important for the database to be as complete and correct as possible. For this reason, many data cleaning tools have been developed to automatically resolve inconsistencies in databases. However, data cleaning tools provide only best-effort results and usually cannot eradicate all errors that may exist in a database. Even more importantly, existing data cleaning tools do not typically address the problem of determining what information is missing from a database.

To tackle these problems, we present QOCO, a novel
query oriented
cleaning system that leverages materialized views that are defined by user queries as a trigger for identifying the remaining incorrect/missing information. Given a user query, QOCO interacts with domain experts (which we model as oracle crowds) to identify potentially wrong or missing answers in the result of the user query, as well as determine and correct the wrong data that is the cause for the error(s). We will demonstrate QOCO over a World Cup Games database, and illustrate the interaction between QOCO and the oracles. Our demo audience will play the role of oracles, and we show how QOCO's underlying operations and optimization mechanisms can effectively prune the search space and minimize the number of questions that need to be posed to accelerate the cleaning process.
A demonstration of the BigDAWG polystore system
Authors: Elmore A. , Duggan J. , Stonebraker M. , Balazinska M. , Cetintemel U. , Gadepally V. , Heer J. , Howe B. , Kepner J. , Kraska T. , Madden S. , Maier D. , Mattson T. , Papadopoulos S. , Parkhurst J. , Tatbul N. , Vartak M. , Zdonik S.
Published: September 2015

This paper presents BigDAWG, a reference implementation of a new architecture for "Big Data" applications. Such applications not only call for large-scale analytics, but also for real-time streaming support, smaller analytics at interactive speeds, data visualization, and cross-storage-system queries. Guided by the principle that "one size does not fit all", we build on top of a variety of storage engines, each designed for a specialized use case. To illustrate the promise of this approach, we demonstrate its effectiveness on a hospital application using data from an intensive care unit (ICU). This complex application serves the needs of doctors and researchers and provides real-time support for streams of patient data. It showcases novel approaches for querying across multiple storage engines, data visualization, and scalable real-time analytics.
Mindtagger
Authors: Shin Jaeho , Ré Christopher , Cafarella Michael
Published: September 2015

End-to-end knowledge base construction systems using statistical inference are enabling more people to automatically extract high-quality domain-specific information from unstructured data. As a result of deploying
DeepDive
framework across several domains, we found new challenges in debugging and improving such end-to-end systems to construct high-quality knowledge bases.
DeepDive
has an iterative development cycle in which users improve the data. To help our users, we needed to develop principles for analyzing the system's error as well as provide tooling for inspecting and labeling various data products of the system. We created guidelines for error analysis modeled after our colleagues' best practices, in which data labeling plays a critical role in every step of the analysis. To enable more productive and systematic data labeling, we created
Mindtagger
, a versatile tool that can be configured to support a wide range of tasks. In this demonstration, we show in detail what data labeling tasks are modeled in our error analysis guidelines and how each of them is performed using
Mindtagger.
Virtual eXist-db
Authors: Dyreson Curtis E. , Bhowmick Sourav S. , Grapp Ryan
Published: September 2015

XQuery programs can be hard to write and port to new data collections because the path expressions in a query aredependenton the hierarchy of the data. We propose to demonstrate a system to liberate query writers from this dependence. Aplug-and-play querycontains a specification of what data the query needs in order to evaluate. We implementedvirtual eXist-dbto support plug-and-play XQuery queries. Our system adds avirtualDocfunction that lets a programmer sketch the hierarchy needed by the query, which may well be different than what the data has, and logically (not physically) transforms the data (with information loss guarantees) to the hierarchy specified by thevirtualDoc.The demonstration will consist of a sequence of XQuery queries using a virtual hierarchy, including queries suggested by the audience. We will also demonstrate a GUI tool to construct a virtual hierarchy.
VIIQ
Authors: Jayaram Nandish , Goyal Sidharth , Li Chengkai
Published: September 2015

We present
VIIQ
(pronounced as wick), an interactive and iterative visual query formulation interface that helps users construct query graphs specifying their exact query intent. Heterogeneous graphs are increasingly used to represent complex relationships in schemaless data, which are usually queried using query graphs. Existing graph query systems offer little help to users in easily choosing the exact labels of the edges and vertices in the query graph.
VIIQ
helps users
easily
specify their
exact
query intent by providing a visual interface that lets them graphically add various query graph components, backed by an edge suggestion mechanism that suggests edges relevant to the user's query intent. In this demo we present: 1) a detailed description of the various features and user-friendly graphical interface of
VIIQ
, 2) a brief description of the edge suggestion algorithm, and 3) a demonstration scenario that we intend to show the audience.
GIS navigation boosted by column stores
Authors: Alvanaki Foteini , Goncalves Romulo , Ivanova Milena , Kersten Martin , Kyzirakos Kostis
Published: September 2015

Earth observation sciences, astronomy, and seismology have large data sets which have inherently rich spatial and geospatial information. In combination with large collections of semantically rich objects which have a large number of thematic properties, they form a new source of knowledge for urban planning, smart cities and natural resource management.

Modeling and storing these properties indicating the relationships between them is best handled in a relational database. Furthermore, the scalability requirements posed by the latest 26-attribute light detection and ranging (LIDAR) data sets are a challenge for file-based solutions.

In this demo we show how to query a 640 billion point data set using a column store enriched with GIS functionality. Through a lightweight and cache conscious secondary index called Imprints, spatial queries performance on a flat table storage is comparable to traditional file-based solutions. All the results are visualised in real time using QGIS.
Gain control over your integration evaluations
Authors: Arocena Patricia C. , Ciucanu Radu , Glavic Boris , Miller Renée J.
Published: September 2015

Integration systems are typically evaluated using a few real-world scenarios (e.g., bibliographical or biological datasets) or using synthetic scenarios (e.g., based on star-schemas or other patterns for schemas and constraints). Reusing such evaluations is a cumbersome task because their focus is usually limited to showcasing a specific feature of an approach. This makes it difficult to compare integration solutions, understand their generality, and understand their performance for different application scenarios. Based on this observation, we demonstrate some of the requirements for developing integration benchmarks. We argue that the major abstractions used for integration problems have converged in the last decade which enables the application of robust empirical methods to integration problems (from schema evolution, to data exchange, to answering queries using views and many more). Specifically, we demonstrate that schema mappings are the main abstraction that now drives most integration solutions and show how a metadata generator can be used to create more credible evaluations of the performance and scalability of data integration systems. We will use the demonstration to evangelize for more robust, shared empirical evaluations of data integration systems.
EFQ
Authors: Bidoit Nicole , Herschel Melanie , Tzompanaki Katerina
Published: September 2015

One important issue in modern database applications is supporting the user with efficient tools to debug and fix queries because such tasks are both time and skill demanding. One particular problem is known as
Why-Not question
and focusses on the reasons for missing tuples from query results. The
EFQ
platform demonstrated here has been designed in this context to efficiently leverage
Why-Not Answers polynomials
, a novel approach that provides the user with complete explanations to Why-Not questions and allows for automatic, relevant query refinements.
A demonstration of TripleProv
Authors: Wylot Marcin , Cudré-Mauroux Philippe , Groth Paul
Published: September 2015

The proliferation of heterogeneous Linked Data on the Web poses new challenges to database systems. In particular, the capacity to store, track, and query provenance data is becoming a pivotal feature of modern triple stores. In this demonstration, we present TripleProv: a new system extending a native RDF store to efficiently handle the storage, tracking and querying of provenance in RDF data. In the following, we give an overview of our approach providing a reliable and understandable specification of the way results were derived from the data and how particular pieces of data were combined to answer the query. Subsequently, we present techniques enabling to tailor queries with provenance data. Finally, we describe our demonstration and how the attendees will be able to interact with our system during the conference.
Wisteria
Authors: Haas Daniel , Krishnan Sanjay , Wang Jiannan , Franklin Michael J. , Wu Eugene
Published: September 2015

Analysts report spending upwards of 80% of their time on problems in data cleaning. The data cleaning process is inherently iterative, with evolving cleaning workflows that start with basic exploratory data analysis on small samples of dirty data, then refine analysis with more sophisticated/expensive cleaning operators (e.g., crowdsourcing), and finally apply the insights to a full dataset. While an analyst often knows at a logical level what operations need to be done, they often have to manage a large search space of physical operators and parameters. We present Wisteria, a system designed to support the iterative development and optimization of data cleaning workflows, especially ones that utilize the crowd. Wisteria separates logical operations from physical implementations, and driven by analyst feedback, suggests optimizations and/or replacements to the analyst's choice of physical implementation. We highlight research challenges in sampling, in-flight operator replacement, and crowdsourcing. We overview the system architecture and these techniques, then provide a demonstration designed to showcase how Wisteria can improve iterative data analysis and cleaning. The code is available at: http://www.sampleclean.org.
CODD
Authors: S. Ashoke , Haritsa Jayant R.
Published: September 2015

The construction and development of the so-called Big Data systems has occupied centerstage in the data management community in recent years. However, there has been comparatively little attention paid to the
testing
of such systems, an essential pre-requisite for successful deployment. This is surprising given that traditional testing techniques, which typically involve construction of representative databases and regression query suites, are completely impractical at Big Data scale -- simply due to the time and space overheads involved in their execution. For instance, consider the situation where a database engineer wishes to evaluate the query optimizer's behavior on a futuristic Big Data setup featuring "yottabyte" (10
24
bytes) sized relational tables. Obviously, just generating this data, let alone storing it, is practically infeasible even on the best of systems.
Vizdom
Authors: Crotty Andrew , Galakatos Alex , Zgraggen Emanuel , Binnig Carsten , Kraska Tim
Published: September 2015

Machine learning (ML) and advanced statistics are important tools for drawing insights from large datasets. However, these techniques often require human intervention to steer computation towards meaningful results. In this demo, we present V
izdom
, a new system for interactive analytics through pen and touch. V
izdom
's frontend allows users to visually compose complex workflows of ML and statistics operators on an interactive whiteboard, and the back-end leverages recent advances in workflow compilation techniques to run these computations at interactive speeds. Additionally, we are exploring approximation techniques for quickly visualizing partial results that incrementally refine over time. This demo will show V
izdom
's capabilities by allowing users to interactively build complex analytics workflows using real-world datasets.
GraphGen
Authors: Xirogiannopoulos Konstantinos , Khurana Udayan , Deshpande Amol
Published: September 2015

Analyzing interconnection structures among the data through the use of
graph algorithms
and
graph analytics
has been shown to provide tremendous value in many application domains. However, graphs are not the primary choice for how most data is currently stored, and users who want to employ graph analytics are forced to extract data from their data stores, construct the requisite graphs, and then use a specialized engine to write and execute their graph analysis tasks. This cumbersome and costly process not only raises barriers in using graph analytics, but also makes it hard to
explore
and
identify
hidden or implicit graphs in the data. Here we demonstrate a system, called G
raph
G
en
, that enables users to declaratively specify graph extraction tasks over relational databases, visually explore the extracted graphs, and write and execute graph algorithms over them, either directly or using existing graph libraries like the widely used
NetworkX
Python library. We also demonstrate how unifying the extraction tasks and the graph algorithms enables significant optimizations that would not be possible otherwise.
DBSeer
Authors: Yoon Dong Young , Mozafari Barzan , Brown Douglas P.
Published: September 2015

The pressing need for achieving and maintaining high performance in database systems has made database administration one of the most stressful jobs in information technology. On the other hand, the increasing complexity of database systems has made qualified database administrators (DBAs) a scarce resource. DBAs are now responsible for an array of demanding tasks; they need to (i) provision and tune their database according to their application requirements, (ii) constantly monitor their database for any performance failures or slowdowns, (iii) diagnose the root cause of the performance problem in an accurate and timely fashion, and (iv) take prompt actions that can restore acceptable database performance.

However, much of the research in the past years has focused on improving the raw performance of the database systems, rather than improving their manageability. Besides sophisticated consoles for monitoring performance and a few auto-tuning wizards, DBAs are not provided with any help other than their own many years of experience. Typically, their only resort is trial-and-error, which is a tedious, ad-hoc and often sub-optimal solution.

In this demonstration, we present DBSeer, a workload intelligence framework that exploits advanced machine learning and causality techniques to aid DBAs in their various responsibilities. DBSeer analyzes large volumes of statistics and telemetry data collected from various log files to provide the DBA with a suite of rich functionalities including performance prediction, performance diagnosis, bottleneck explanation, workload insight, optimal admission control, and what-if analysis. In this demo, we showcase various features of DBSeer by predicting and analyzing the performance of a live database system. Will also reproduce a number of realistic performance problems in the system, and allow the audience to use DBSeer to quickly diagnose and resolve their root cause.
Real time analytics
Authors: Kejariwal Arun , Kulkarni Sanjeev , Ramasamy Karthik
Published: September 2015

V
elocity is one of the
4 Vs
commonly used to characterize Big Data [5]. In this regard, Forrester remarked the following in Q3 2014 [8]:
"The high velocity, white-water flow of data from innumerable real-time data sources such as market data, Internet of Things, mobile, sensors, click-stream, and even transactions remain largely unnavigated by most firms. The opportunity to leverage streaming analytics has never been greater."
Example use cases of streaming analytics include, but not limited to: (a) visualization of business metrics in real-time (b) facilitating highly personalized experiences (c) expediting response during emergencies. Streaming analytics is extensively used in a wide variety of domains such as healthcare, e-commerce, financial services, telecommunications, energy and utilities, manufacturing, government and transportation.

In this tutorial, we shall present an in-depth overview of streaming analytics -- applications, algorithms and platforms -- landscape. We shall walk through how the field has evolved over the last decade and then discuss the current challenges -- the impact of the other three
V
s, viz.,
V
olume,
V
ariety and
V
eracity, on Big Data streaming analytics. The tutorial is intended for both researchers and practitioners in the industry. We shall also present state-of-the-affairs of streaming analytics at Twitter.
On uncertain graphs modeling and queries
Authors: Khan Arijit , Chen Lei
Published: September 2015

Large-scale, highly-interconnected networks pervade both our society and the natural world around us. Uncertainty, on the other hand, is inherent in the underlying data due to a variety of reasons, such as noisy measurements, lack of precise information needs, inference and prediction models, or explicit manipulation, e.g., for privacy purposes. Therefore, uncertain, or probabilistic, graphs are increasingly used to represent noisy linked data in many emerging application scenarios, and they have recently become a hot topic in the database research community. While many classical graph algorithms such as reachability and shortest path queries become #
P
-complete, and hence, more expensive in uncertain graphs; various complex queries are also emerging over uncertain networks, such as pattern matching, information diffusion, and influence maximization queries. In this tutorial, we discuss the sources of uncertain graphs and their applications, uncertainty modeling, as well as the complexities and algorithmic advances on uncertain graphs processing in the context of both classical and emerging graph queries. We emphasize the current challenges and highlight some future research directions.
A time machine for information
Authors: Dong Xin Luna , Tan Wang-Chiew
Published: September 2015

With the abundant availability of information one can mine from the Web today, there is increasing interest to develop a complete understanding of the history of an entity (i.e., a person, a company, a music genre, a country, etc.) (see, for example, [7, 9, 10, 11]) and to depict trends over time [5, 12, 13]. This, however, remains a largely difficult and manual task despite more than a couple of decades of research in the areas of temporal databases and data integration.
Truth discovery and crowdsourcing aggregation
Authors: Gao Jing , Li Qi , Zhao Bo , Fan Wei , Han Jiawei
Published: September 2015

In the era of Big Data, data entries, even describing the same objects or events, can come from a variety of sources, where a data source can be a web page, a database or a person. Consequently, conflicts among sources become inevitable. To resolve the conflicts and achieve high quality data, truth discovery and crowdsourcing aggregation have been studied intensively. However, although these two topics have a lot in common, they are studied separately and are applied to different domains. To answer the need of a systematic introduction and comparison of the two topics, we present an organized picture on truth discovery and crowdsourcing aggregation in this tutorial. They are compared on both theory and application levels, and their related areas as well as open questions are discussed.
Engineering database hardware and software together
Authors: Loaiza Juan
Published: September 2015

Since its inception, Oracle's database software primarily ran on customer configured off-the-shelf hardware. A decade ago, the architecture of conventional systems started to become a bottleneck and Oracle developed the Oracle Exadata Database Machine to optimize the full hardware and software stack for database workloads. Exadata is based on a scale-out architecture of database servers and storage servers that optimizes both OLTP and Analytic workloads while hosting hundreds of databases simultaneously on the same system. By using database specific protocols for storage and networking we bypass limitations imposed by conventional network and storage layers. Exadata is now deployed at thousands of Enterprises including 4 of the 5 largest banks, telecoms, and retailers for varied workloads such as interbank funds transfers, e-commerce, ERP, Cloud SaaS applications, and petabyte data warehouses.

Five years ago, Oracle initiated a project to extend our database stack beyond software and systems and into the architecture of the microprocessor itself. The goal of this effort is to dramatically improve the performance, reliability and cost effectiveness of a new generation of database machines. The new SPARC M7 processor is the first step. The M7 is an extraordinarily fast conventional processor with 32-cores per socket and an extremely high bandwidth memory system. Added to its conventional processing capabilities are 32 custom on-chip database co-processors that run database searches at full memory bandwidth rates, and decompress data in real-time to increase memory bandwidth and capacity. Further, the M7 implements innovative fine-grained memory protection to secure sensitive business data.

In the presentation we will describe how Oracle's engineering teams integrate software and hardware at all levels to achieve breakthrough performance, reliability, and security for the database and rest of the modern data processing stack.
Big plateaus of big data on the big island
Authors: Walter Todd
Published: September 2015

In ancient texts, 40 was a magic number. It meant a "lot" or "a long time." 40 years represented the time it took for a new generation to arise. A look back at 40 years of VLDB suggests that this applies to database researchers as well -- the young researchers of the early VLDBs are now the old folks of the database world, and a new generation is creating afresh. Over this period many plateaus of "Big Data" have challenged the database community and been conquered. But there is still no free lunch -- database research is really the science of trade-offs, many of which are no different today than 40 years ago. And of course the evolution of hardware technology continues to swing the trade-off pendulum while enabling new plateaus to be reached. Todd will take a look back at customer big data plateaus of the past. He will look at where we are today, then use his crystal ball and the lessons of the past to extrapolate the next several plateaus -- how they will be the same and how will they be different. Along the way we will have a little fun with some VLDB and Teradata history.
Lipschitz conditions for the generalized Fourier transform associated with the Jacobi–Cherednik operator on ℝ
Authors: Ghabi Rabiaa , Mili Maher
Published: September 2015

In this paper, we consider the generalized Fourier transform associated with the Jacobi–Cherednik operator on ℝ and we give conditions of quite different kind for a function to have a transform belonging to certain
Developing Chinese Complimenting in a Study Abroad Program
Authors: Jin Li
Published: September 2015

This article reports on an empirical study investigating what and how two college-level American learners of Mandarin Chinese developed their own understanding of the Chinese complimenting speech act while participating in a 7-week intensive language study abroad program. A case study approach was adopted with a focus on participants’ self-reflection data supplemented with the researcher’s observation data to unpack each participant’s pragmatic developmental process throughout the program. Guided by sociocultural theory, the study uncovered the dynamic, complex and highly individualized developmental process each participant experienced. The findings revealed that Chinese people’s special treatment of the two American students provided more constraints than opportunities for them to acquire Chinese complimenting. However, the learners’ own motivation and approaches to learning also significantly shaped their learning process and outcomes. Pedagogical suggestions for Chinese pragmatic development in study abroad contexts are provided.
Discrete dislocation modeling of stress corrosion cracking in an iron
Authors: Adlakha Ilaksh , Sadananda Kuntimaddi , Solanki Kiran N.
Published: September 2015

Material strengthening and embrittlement are controlled by interactions between dislocations and hydrogen that alter the observed deformation mechanisms. In this work, we used an energetics approach to differentiate two fundamental stress corrosion mechanisms in iron, namely, hydrogen-enhanced localized plasticity and hydrogen-enhanced decohesion. Considering the small-scale yielding condition, we use a discrete dislocation framework with line dislocations to simulate the crack-tip plastic behavior. The crack growth was modeled using the change in surface energies (cohesive zone laws) due to hydrogen segregation. The changes in the surface energies as a function of hydrogen concentration are computed using atomistic simulations. Results indicate that, when hydrogen concentrations are low, crack growth occurs by alternating mechanisms of cleavage and slip. However, as the hydrogen concentrations increased above some critical value, the crack grows predominately by the cleavage-based decohesion process.
Synthesis and characterization of N-glucosylated dithiadiazepine derivatives through carbon-sulfur bond formation
Authors: Chavan Snehal A. , Ulhe Avinash G. , Berad Baliram N.
Published: September 2015

New 4,7-bis(arylamino)-2-tetra-O-acetyl-β-d-glucopyranosylimino-1,3,5,6-dithiadiazepines were synthesized via reaction of N-tetra-O-acetyl-β-d-glucopyranosyl isocyanodichloride with 1,6-diaryl-2,5-dithio-bis-ureas without using any catalyst. Thus, the synthesis of 7-membered heterocycles containing two sulfur and two nitrogen atoms through carbon-sulfur bond formation was explored. The chemical structures of these new compounds were elucidated by IR, 1H NMR, 13C NMR, mass spectral, and elemental analyses.