bluuebunny/crossref_metadata_2025 huggingface dataset:
A framework for protecting worker location privacy in spatial crowdsourcing
Authors: To Hien , Ghinita Gabriel , Shahabi Cyrus
Published: May 2015

Spatial Crowdsourcing (SC)
is a transformative platform that engages individuals, groups and communities in the act of collecting, analyzing, and disseminating environmental, social and other spatio-temporal information. The objective of SC is to outsource a set of spatio-temporal tasks to a set of
workers
, i.e., individuals with mobile devices that perform the tasks by physically traveling to specified locations of interest. However, current solutions require the workers, who in many cases are simply volunteering for a cause, to disclose their locations to untrustworthy entities. In this paper, we introduce a framework for protecting location privacy of workers participating in SC tasks. We argue that existing location privacy techniques are not sufficient for SC, and we propose a mechanism based on differential privacy and geocasting that achieves effective SC services while offering privacy guarantees to workers. We investigate analytical models and task assignment strategies that balance multiple crucial aspects of SC functionality, such as task completion rate, worker travel distance and system overhead. Extensive experimental results on real-world datasets show that the proposed technique protects workers' location privacy without incurring significant performance metrics penalties.
The case for personal data-driven decision making
Authors: Duggan Jennie
Published: May 2015

Data-driven decision making (D3M) has shown great promise in professional pursuits such as business and government. Here, policymakers collect and analyze data to make their operations more efficient and equitable. Progress in bringing the benefits of D3M to everyday life has been slow. For example, a student asks, "If I pursue an undergraduate degree at this university, what are my expected lifetime earnings?". Presently there is no principled way to search for this, because an accurate answer depends on the student and school.

Such queries are personalized, winnowing down large datasets for specific circumstances, rather than applying well-defined predicates. They predict decision outcomes by extrapolating from relevant examples. This vision paper introduces a new approach to D3M that is designed to empower the individual to make informed choices. Here, we highlight research opportunities for the data management community arising from this proposal.
Ibex
Authors: Woods Louis , István Zsolt , Alonso Gustavo
Published: May 2015

Modern data appliances face severe bandwidth bottlenecks when moving vast amounts of data from storage to the query processing nodes. A possible solution to mitigate these bottlenecks is query off-loading to an
intelligent storage engine
, where partial or whole queries are pushed down to the storage engine. In this paper, we present
Ibex
, a prototype of an intelligent storage engine that supports off-loading of complex query operators. Besides increasing performance,
Ibex
also reduces energy consumption, as it uses an FPGA rather than conventional CPUs to implement the off-load engine.
Ibex
is a
hybrid
engine, with dedicated hardware that evaluates SQL expressions at line-rate and a software fallback for tasks that the hardware engine cannot handle.
Ibex
supports GROUP BY aggregation, as well as
projection
- and
selection
- based filtering. GROUP BY aggregation has a higher impact on performance but is also a more challenging operator to implement on an FPGA.
Repairing vertex labels under neighborhood constraints
Authors: Song Shaoxu , Cheng Hong , Yu Jeffrey Xu , Chen Lei
Published: May 2015

A broad class of data, ranging from similarity networks, workflow networks to protein networks, can be modeled as graphs with data values as vertex labels. The vertex labels (data values) are often dirty for various reasons such as typos or erroneous reporting of results in scientific experiments.
Neighborhood constraints
, specifying label pairs that are allowed to appear on adjacent vertexes in the graph, are employed to detect and repair erroneous vertex labels. In this paper, we study the problem of repairing vertex labels to make graphs satisfy neighborhood constraints. Unfortunately, the relabeling problem is proved to be NP hard, which motivates us to devise approximation methods for repairing, and identify interesting special cases (star and clique constraints) that can be efficiently solved. We propose several approximate repairing algorithms including greedy heuristics, contraction method and a hybrid approach. The performances of algorithms are also analyzed for the special case. Our extensive experimental evaluation, on both synthetic and real data, demonstrates the effectiveness of eliminating frauds in several types of application networks. Remarkably, the hybrid method performs well in practice, i.e., guarantees termination, while achieving high effectiveness at the same time.
Code generation for efficient query processing in managed runtimes
Authors: Nagel Fabian , Bierman Gavin , Viglas Stratis D.
Published: May 2015

In this paper we examine opportunities arising from the convergence of two trends in data management: in-memory database systems (imdbs), which have received renewed attention following the availability of affordable, very large main memory systems; and language-integrated query, which transparently integrates database queries with programming languages (thus addressing the famous 'impedance mismatch' problem). Language-integrated query not only gives application developers a more convenient way to query external data sources like imdbs, but also to use the same querying language to query an application's in-memory collections. The latter offers further transparency to developers as the query language and all data is represented in the data model of the host programming language. However, compared to imdbs, this additional freedom comes at a higher cost for query evaluation. Our vision is to improve in-memory query processing of application objects by introducing database technologies to managed runtimes.

We focus on querying and we leverage query compilation to improve query processing on application objects. We explore different query compilation strategies and study how they improve the performance of query processing over application data. We take C# as the host programming language as it supports language-integrated query through the linq framework. Our techniques deliver significant performance improvements over the default linq implementation. Our work makes important first steps towards a future where data processing applications will commonly run on machines that can store their entire datasets in-memory, and will be written in a single programming language employing language-integrated query and imdb-inspired runtimes to provide transparent and highly efficient querying.
Aggregate estimation over dynamic hidden web databases
Authors: Liu Weimo , Thirumuruganathan Saravanan , Zhang Nan , Das Gautam
Published: May 2015

Many databases on the web are "hidden" behind (i.e., accessible only through) their restrictive, form-like, search interfaces. Recent studies have shown that it is possible to estimate aggregate query answers over such hidden web databases by issuing a small number of carefully designed search queries through the restrictive web interface. A problem with these existing work, however, is that they all assume the underlying database to be static, while most real-world web databases (e.g., Amazon, eBay) are frequently updated. In this paper, we study the novel problem of estimating/tracking aggregates over dynamic hidden web databases while adhering to the stringent query-cost limitation they enforce (e.g., at most 1,000 search queries per day). Theoretical analysis and extensive real-world experiments demonstrate the effectiveness of our proposed algorithms and their superiority over baseline solutions (e.g., the repeated execution of algorithms designed for static web databases).
Adaptive query processing on RAW data
Authors: Karpathiotakis Manos , Branco Miguel , Alagiannis Ioannis , Ailamaki Anastasia
Published: May 2015

Database systems deliver impressive performance for large classes of workloads as the result of decades of research into optimizing database engines. High performance, however, is achieved at the cost of versatility. In particular, database systems only operate efficiently over loaded data, i.e., data converted from its original raw format into the system's internal data format. At the same time, data volume continues to increase exponentially and data varies increasingly, with an escalating number of new formats. The consequence is a growing impedance mismatch between the original structures holding the data in the raw files and the structures used by query engines for efficient processing. In an ideal scenario, the query engine would seamlessly adapt itself to the data and ensure efficient query processing regardless of the input data formats, optimizing itself to each instance of a file and of a query by leveraging information available at query time. Today's systems, however, force data to adapt to the query engine during data loading.

This paper proposes adapting the query engine to the formats of raw data. It presents RAW, a prototype query engine which enables querying heterogeneous data sources transparently. RAW employs Just-In-Time access paths, which efficiently couple heterogeneous raw files to the query engine and reduce the overheads of traditional general-purpose scan operators. There are, however, inherent overheads with accessing raw data directly that cannot be eliminated, such as converting the raw values. Therefore, RAW also uses column shreds, ensuring that we pay these costs only for the subsets of raw data strictly needed by a query. We use RAW in a real-world scenario and achieve a two-order of magnitude speedup against the existing hand-written solution.
Storing and querying tree-structured records in Dremel
Authors: Afrati Foto N. , Delorey Dan , Pasumansky Mosha , Ullman Jeffrey D.
Published: May 2015

In Dremel, data is stored as nested relations. The schema for a relation is a tree, all of whose nodes are attributes, and whose leaf attributes hold values. We explore filter and aggregate queries that are given in the Dremel dialect of SQL. Complications arise because of
repeated
attributes, i.e., attributes that are allowed to have more than one value. We focus on the common class of Dremel queries that are processed on column-stored data in a way that results in query processing time that is linear on the size of the relevant data, i.e., data in the columns that participate in the query. We formally define the data model, the query language and the algorithms for query processing in column-stored data. The concepts of repetition context and semi-flattening are introduced here and play a central role in understanding this class of queries and their algorithms.
Matching titles with cross title web-search enrichment and community detection
Authors: Londhe Nikhil , Gopalakrishnan Vishrawas , Zhang Aidong , Ngo Hung Q. , Srihari Rohini
Published: May 2015

Title matching refers roughly to the following problem. We are given two strings of text obtained from different data sources. The texts refer to some underlying physical entities and the problem is to report whether the two strings refer to the same physical entity or not. There are manifestations of this problem in a variety of domains, such as product or bibliography matching, and location or person disambiguation.

We propose a new approach to solving this problem, consisting of two main components. The first component uses Web searches to "enrich" the given pair of titles: making titles that refer to the same physical entity more similar, and those which do not, much less similar. A notion of similarity is then measured using the second component, where the tokens from the two titles are modelled as vertices of a "social" network graph. A "strength of ties" style of clustering algorithm is then applied on this to see whether they form one cohesive "community" (matching titles), or separately clustered communities (mismatching titles). Experimental results confirm the effectiveness of our approach over existing title matching methods across several input domains.
Bounded conjunctive queries
Authors: Cao Yang , Fan Wenfei , Wo Tianyu , Yu Wenyuan
Published: May 2015

A query
Q
is said to be
effectively bounded
if for all datasets
D
, there exists a subset
D

Q

of
D
such that
Q
(
D
) =
Q
(
D

Q

), and the size of
DQ
and time for fetching
D

Q

are
independent of
the size of
D
. The need for studying such queries is evident, since it allows us to compute
Q
(
D
) by accessing a bounded dataset
D

Q

,
regardless of
how big
D
is. This paper investigates effectively bounded conjunctive queries (SPC) under an access schema
A
, which specifies indices and cardinality constraints commonly used. We provide characterizations (sufficient and necessary conditions) for determining whether an SPC query
Q
is effectively bounded under
A
. We study several problems for deciding whether
Q
is bounded, and if not, for identifying a minimum set of parameters of
Q
to instantiate and make
Q
bounded. We show that these problems range from quadratic-time to NP-complete, and develop efficient (heuristic) algorithms for them. We also provide an algorithm that, given an effectively bounded SPC query
Q
and an access schema
A
, generates a query plan for evaluating
Q
by accessing a bounded amount of data in any (possibly big) dataset. We experimentally verify that our algorithms substantially reduce the cost of query evaluation.
A system for management and analysis of preference data
Authors: Jacob Marie , Kimelfeld Benny , Stoyanovich Julia
Published: May 2015

Preference data arises in a wide variety of domains. Over the past decade, we have seen a sharp increase in the volume of preference data, in the diversity of applications that use it, and in the richness of preference data analysis methods. Examples of applications include rank aggregation in genomic data analysis, management of votes in elections, and recommendation systems in e-commerce. However, little attention has been paid to the challenges of building a system for preference-data management, which would help incorporate sophisticated analytics into larger applications, support computational abstractions for usability by data scientists, and enable scaling up to modern volumes. This vision paper proposes a management system for preference data that aims to address these challenges. We adopt the relational database model, and propose extensions that are specialized to handling preference data. Specifically, we introduce a special type of a relation that is designed for preference data, and describe composable operators on preference relations that can be embedded in SQL statements, for convenient reuse across applications.
Optimal security-aware query processing
Authors: Guarnieri Marco , Basin David
Published: May 2015

Security-Aware Query Processing is the problem of computing answers to queries in the presence of access control policies. We present general impossibility results for the existence of optimal algorithms for Security-Aware Query Processing and classify query languages for which such algorithms exist. In particular, we show that for the relational calculus there are no optimal algorithms, whereas optimal algorithms exist for some of its fragments, such as the existential fragment.

We also establish relationships between two different models of Fine-Grained Access Control, called Truman and Non-Truman models, which have been previously presented in the literature as distinct. For optimal Security-Aware Query Processing, we show that the Non-Truman model is a special case of the Truman model for boolean queries in the relational calculus, moreover the two models coincide for more powerful languages, such as the relational calculus with aggregation operators. In contrast, these two models are distinct for non-boolean queries.
MRTuner
Authors: Shi Juwei , Zou Jia , Lu Jiaheng , Cao Zhao , Li Shiqiang , Wang Chen
Published: May 2015

MapReduce based data-intensive computing solutions are increasingly deployed as production systems. Unlike Internet companies who invent and adopt the technology from the very beginning, traditional enterprises demand easy-to-use software due to the limited capabilities of administrators. Automatic job optimization software for MapReduce is a promising technique to satisfy such requirements. In this paper, we introduce a toolkit from IBM, called
MRTuner
, to enable holistic optimization for MapReduce jobs. In particular, we propose a novel
Producer-Transporter-Consumer
(PTC) model, which characterizes the tradeoffs in the parallel execution among tasks. We also carefully investigate the complicated relations among about twenty parameters, which have significant impact on the job performance. We design an efficient search algorithm to find the optimal execution plan. Finally, we conduct a thorough experimental evaluation on two different types of clusters using the HiBench suite which covers various Hadoop workloads from GB to TB size levels. The results show that the search latency of
MRTuner
is a few orders of magnitude faster than that of the state-of-the-art cost-based optimizer, and the effectiveness of the optimized execution plan is also significantly improved.
TPC-DI
Authors: Poess Meikel , Rabl Tilmann , Jacobsen Hans-Arno , Caufield Brian
Published: May 2015

Historically, the process of synchronizing a decision support system with data from operational systems has been referred to as Extract, Transform, Load (ETL) and the tools supporting such process have been referred to as ETL tools. Recently, ETL was replaced by the more comprehensive acronym, data integration (DI). DI describes the process of extracting and combining data from a variety of data source formats, transforming that data into a unified data model representation and loading it into a data store. This is done in the context of a variety of scenarios, such as data acquisition for business intelligence, analytics and data warehousing, but also synchronization of data between operational applications, data migrations and conversions, master data management, enterprise data sharing and delivery of data services in a service-oriented architecture context, amongst others. With these scenarios relying on up-to-date information it is critical to implement a highly performing, scalable and easy to maintain data integration system. This is especially important as the complexity, variety and volume of data is constantly increasing and performance of data integration systems is becoming very critical. Despite the significance of having a highly performing DI system, there has been no industry standard for measuring and comparing their performance. The TPC, acknowledging this void, has released TPC-DI, an innovative benchmark for data integration. This paper motivates the reasons behind its development, describes its main characteristics including workload, run rules, metric, and explains key decisions.
Real-time twitter recommendation
Authors: Gupta Pankaj , Satuluri Venu , Grewal Ajeet , Gurumurthy Siva , Zhabiuk Volodymyr , Li Quannan , Lin Jimmy
Published: May 2015

We describe a production Twitter system for generating relevant, personalized, and timely recommendations based on observing the temporally-correlated actions of each user's followings. The system currently serves millions of recommendations daily to tens of millions of mobile users. The approach can be viewed as a specific instance of the novel problem of online motif detection in large dynamic graphs. Our current solution partitions the graph across a number of machines, and with the construction of appropriate data structures, motif detection can be translated into the lookup and intersection of adjacency lists in each partition. We conclude by discussing a generalization of the problem that perhaps represents a new class of data management systems.
Fast foreign-key detection in Microsoft SQL server PowerPivot for Excel
Authors: Chen Zhimin , Narasayya Vivek , Chaudhuri Surajit
Published: May 2015

Microsoft SQL Server PowerPivot for Excel, or PowerPivot for short, is an in-memory business intelligence (BI) engine that enables Excel users to interactively create pivot tables over large data sets imported from sources such as relational databases, text files and web data feeds. Unlike traditional pivot tables in Excel that are defined on a single table, PowerPivot allows analysis over multiple tables connected via foreign-key joins. In many cases however, these foreign-key relationships are not known a priori, and information workers are often not be sophisticated enough to define these relationships. Therefore, the ability to automatically discover foreign-key relationships in PowerPivot is valuable, if not essential. The key challenge is to perform this detection
interactively
and with
high precision
even when data sets scale to hundreds of millions of rows and the schema contains tens of tables and hundreds of columns. In this paper, we describe techniques for fast foreign-key detection in PowerPivot and experimentally evaluate its accuracy, performance and scale on both synthetic benchmarks and real-world data sets. These techniques have been incorporated into PowerPivot for Excel.
Execution primitives for scalable joins and aggregations in map reduce
Authors: Vemuri Srinivas , Varshney Maneesh , Puttaswamy Krishna , Liu Rui
Published: May 2015

Analytics on Big Data is critical to derive business insights and drive innovation in today's Internet companies. Such analytics involve complex computations on large datasets, and are typically performed on MapReduce based frameworks such as Hive and Pig. However, in our experience, these systems are still quite limited in performing at scale. In particular, calculations that involve complex joins and aggregations, e.g. statistical calculations, scale poorly on these systems.

In this paper we propose novel primitives for scaling such calculations. We propose a new data model for organizing datasets into
calculation data units
that are organized based on user-defined cost functions. We propose new operators that take advantage of these organized data units to significantly speed up joins and aggregations. Finally, we propose strategies for dividing the aggregation load uniformly across worker processes that are very effective in avoiding skews and reducing (or in some cases even removing) the associated overheads.

We have implemented all our proposed primitives in a framework called Rubix, which has been in production at LinkedIn for nearly a year. Rubix powers several applications and processes TBs of data each day. We have seen remarkable improvements in speed and cost of complex calculations due to these primitives.
CAP limits in telecom subscriber database design
Authors: Arauz Javier
Published: May 2015

While the notion of a Distributed DBMS has been familiar to the IT industry for several decades, within telecom networks the subscriber data management based on DDBMS technology is a novel addition to a service provider's infrastructure.

Service providers are used to telecom networks that are efficient, reliable and easy to maintain and operate, in part thanks to the node model used in designing such networks. A DDBMS spanning a large geographical area however incurs into distributed systems issues not previously seen in telecom networks.

Identifying and delivering the right set of trade-offs that satisfies the service providers' needs while staying within the known physical bounds of a distributed system is therefore crucial if DDBMS are to conquer the subscriber management space within telecom networks.
Advanced join strategies for large-scale distributed computation
Authors: Bruno Nicolas , Kwon YongChul , Wu Ming-Chuan
Published: May 2015

Companies providing cloud-scale data services have increasing needs to store and analyze massive data sets (e.g., search logs, click streams, and web graph data). For cost and performance reasons, processing is typically done on large clusters of thousands of commodity machines by using high level scripting languages. In the recent past, there has been significant progress in adapting well-known techniques from traditional relational DBMSs to this new scenario. However, important challenges remain open. In this paper we study the very common join operation, discuss some unique challenges in the large-scale distributed scenario, and explain how to efficiently and robustly process joins in a distributed way. Specifically, we introduce novel execution strategies that leverage opportunities not available in centralized scenarios, and others that robustly handle data skew. We report experimental validations of our approaches on Scope production clusters, which power the Applications and Services Group at Microsoft.
DGFIndex for smart grid
Authors: Liu Yue , Hu Songlin , Rabl Tilmann , Liu Wantao , Jacobsen Hans-Arno , Wu Kaifeng , Chen Jian , Li Jintao
Published: May 2015

In Smart Grid applications, as the number of deployed electric smart meters increases, massive amounts of valuable meter data is generated and collected every day. To enable reliable data collection and make business decisions fast, high throughput storage and high-performance analysis of massive meter data become crucial for grid companies. Considering the advantage of high efficiency, fault tolerance, and price-performance of Hadoop and Hive systems, they are frequently deployed as underlying platform for big data processing. However, in real business use cases, these data analysis applications typically involve multidimensional range queries (MDRQ) as well as batch reading and statistics on the meter data. While Hive is high-performance at complex data batch reading and analysis, it lacks efficient indexing techniques for MDRQ.

In this paper, we propose DGFIndex, an index structure for Hive that efficiently supports MDRQ for massive meter data. DGFIndex divides the data space into cubes using the grid file technique. Unlike the existing indexes in Hive, which stores all combinations of multiple dimensions, DGFIndex only stores the information of cubes. This leads to smaller index size and faster query processing. Furthermore, with pre-computing user-defined aggregations of each cube, DGFIndex only needs to access the boundary region for aggregation query. Our comprehensive experiments show that DGFIndex can save significant disk space in comparison with the existing indexes in Hive and the query performance with DGFIndex is 2-50 times faster than existing indexes in Hive and HadoopDB for aggregation query, 2-5 times faster than both for non-aggregation query, 2-75 times faster than scanning the whole table in different query selectivity.
MESA
Authors: Zheng Yuxin , Bao Zhifeng , Shou Lidan , Tung Anthony K. H.
Published: May 2015

Geo-textual data are ubiquitous these days. Recent study on spatial keyword search focused on the processing of queries which retrieve objects that match certain keywords within a spatial region. To ensure effective data retrieval, various extensions were done including the tolerance of errors in keyword matching and the search-as-you-type feature using prefix matching. We present MESA, a map application to support different variants of spatial keyword query. In this demonstration, we adopt the autocompletion paradigm that generates the initial query as a prefix matching query. If there are few matching results, other variants are performed as a form of relaxation that reuses the processing done in earlier phases. The types of relaxation allowed include spatial region expansion and exact/approximate prefix/substring matching. MESA adopts the client-server architecture. It provides fuzzy type-ahead search over geo-textual data. The core of MESA is to adopt a unifying search strategy, which incrementally applies the relaxation in an appropriate order to maximize the efficiency of query processing. In addition, MESA equips a user-friendly interface to interact with users and visualize results. MESA also provides customized search to meet the needs of different users.
PDQ
Authors: Benedikt Michael , Leblay Julien , Tsamoura Efthymia
Published: May 2015

The data needed to answer queries is often available through Web-based APIs. Indeed, for a given query there may be many Web-based sources which can be used to answer it, with the sources overlapping in their vocabularies, and differing in their access restrictions (required arguments) and cost. We introduce PDQ (Proof-Driven Query Answering), a system for determining a query plan in the presence of web-based sources. It is: (i)
constraint-aware
-- exploiting relationships between sources to rewrite an expensive query into a cheaper one, (ii)
access-aware
-- abiding by any access restrictions known in the sources, and (iii)
cost-aware
-- making use of any cost information that is available about services. PDQ takes the novel approach of
generating query plans from proofs that a query is answerable.
We demonstrate the use of PDQ and its effectiveness in generating low-cost plans.
HDBTracker
Authors: Liu Weimo , Suhaim Saad Bin , Thirumuruganathan Saravanan , Zhang Nan , Das Gautam , Jaoua Ali
Published: May 2015

Numerous web databases, e.g., amazon.com, eBay.com, are "hidden" behind (i.e., accessible only through) their restrictive search and browsing interfaces. This demonstration showcases HDBTracker, a web-based system that reveals and tracks (the changes of) user-specified aggregate queries over such hidden web databases, especially those that are frequently updated, by issuing a small number of search queries through the public web interfaces of these databases. The ability to track and monitor aggregates has applications over a wide variety of domains - e.g., government agencies can track COUNT of openings at online job hunting websites to understand key economic indicators, while businesses can track the AVG price of a product over a basket of e-commerce websites to understand the competitive landscape and/or material costs. A key technique used in HDBTracker is RS-ESTIMATOR, the first algorithm that can efficiently monitor changes to aggregate query answers over a hidden web database.
BSMA
Authors: Xia Fan , Li Ye , Yu Chengcheng , Ma Haixin , Qian Weining
Published: May 2015

The demonstration of a benchmark, named as BSMA, for Benchmarking Social Media Analytics, is introduced in this paper. BSMA is designed to benchmark data management systems supporting analytical queries over social media. It is different to existing benchmarks in that: 1) Both real-life data and a synthetic data generator are provided. The real-life dataset contains a social network of 1.6 million users, and all their tweeting and retweeting activities. The data generator can generate both social networks and synthetic timelines that follow data distributions determined by predefined parameters. 2) A set of workloads are provided. The data generator is in responsible for producing updates. A workload generator produces queries based on predefined query templates by generating query arguments online. BSMA workloads cover a large amount of queries with graph operations, temporal queries, hotspot queries, and aggregate queries. Furthermore, the argument generator is capable of sampling data items in the timeline following power-law distribution online. 3) A toolkit is provided to measure and report the performance of systems that implement the benchmark. Furthermore, a prototype system based on dataset and workloads of BSMA is also implemented. The demonstration will include two parts, i.e. the internals of data and workload generator, as well as the performance testing of reference implementations.
SeeDB
Authors: Vartak Manasi , Madden Samuel , Parameswaran Aditya , Polyzotis Neoklis
Published: May 2015

Data analysts operating on large volumes of data often rely on visualizations to interpret the results of queries. However, finding the right visualization for a query is a laborious and time-consuming task. We demonstrate SeeDB, a system that partially automates this task: given a query, SeeDB explores the space of all possible visualizations, and automatically identifies and recommends to the analyst those visualizations it finds to be most "interesting" or "useful". In our demonstration, conference attendees will see SeeDB in action for a variety of queries on multiple real-world datasets.
PackageBuilder
Authors: Brucato Matteo , Ramakrishna Rahul , Abouzied Azza , Meliou Alexandra
Published: May 2015

In this demo, we present PackageBuilder, a system that extends database systems to support package queries. A package is a collection of tuples that individually satisfy base constraints and collectively satisfy global constraints. The need for package support arises in a variety of scenarios: For example, in the creation of meal plans, users are not only interested in the nutritional content of individual meals (base constraints), but also care to specify daily consumption limits and control the balance of the entire plan (global constraints). We introduce PaQL, a declarative SQL-based package query language, and the interface abstractions which allow users to interactively specify package queries and easily navigate through their results. To efficiently evaluate queries, the system employs pruning and heuristics, as well as state-of-the-art constraint optimization solvers. We demonstrate PackageBuilder by allowing attendees to interact with the system's interface, to define PaQL queries and to observe how query evaluation is performed.
S-Store
Authors: Cetintemel Ugur , Du Jiang , Kraska Tim , Madden Samuel , Maier David , Meehan John , Pavlo Andrew , Stonebraker Michael , Sutherland Erik , Tatbul Nesime , Tufte Kristin , Wang Hao , Zdonik Stanley
Published: May 2015

First-generation streaming systems did not pay much attention to state management via ACID transactions (e.g., [3, 4]). S-Store is a data management system that combines OLTP transactions with stream processing. To create S-Store, we begin with H-Store, a main-memory transaction processing engine, and add primitives to support streaming. This includes triggers and transaction workflows to implement push-based processing, windows to provide a way to bound the computation, and tables with hidden state to implement scoping for proper isolation. This demo explores the benefits of this approach by showing how a naïve implementation of our benchmarks using only H-Store can yield incorrect results. We also show that by exploiting push-based semantics and our implementation of triggers, we can achieve significant improvement in transaction throughput. We demo two modern applications: (i) leaderboard maintenance for a version of "American Idol", and (ii) a city-scale bicycle rental scenario.
CLEar
Authors: Xie Runquan , Zhu Feida , Ma Hui , Xie Wei , Lin Chen
Published: May 2015

We describe our demonstration of CLEar (
Cl
airaudient
Ear
), a real-time online platform for detecting, monitoring, summarizing, contextualizing and visualizing bursty and viral events, those triggering a sudden surge of public interest and going viral on micro-blogging platforms. This task is challenging for existing methods as they either use complicated topic models to analyze topics in a off-line manner or define temporal structure of fixed granularity on the data stream for online topic learning, leaving them hardly scalable for real-time stream like that of Twitter. In this demonstration of CLEar, we present a three-stage system: First, we show a real-time bursty event detection module based on a data-sketch topic model which makes use of acceleration of certain stream quantities as the indicators of topic burstiness to trigger efficient topic inference. Second, we demonstrate popularity prediction for the detected bursty topics and event summarization based on clustering related topics detected in successive time periods. Third, we illustrate CLEar's module for contextualizing and visualizing the event evolution both along time-line and across other news media to offer an easier understanding of the events.
Forward
Authors: Fu Yupeng , Ong Kian Win , Papakonstantinou Yannis , Zamora Erick
Published: May 2015

While Ajax programming and the plethora of JavaScript component libraries enable high-quality Uls in web applications, integrating them with page data is laborious and error-prone as a developer has to handcode incremental modifications with trigger-based programming and manual coordination of data dependencies. The FORWARD web framework simplifies the development of Ajax applications through declarative, state-based templates. This declarative, data-centric approach is characterized by the principle of logical/physical independence, which the database community has often deployed successfully. It enables FORWARD to leverage database techniques, such as incremental view maintenance, updatable views, capability-based component wrappers and cost-based optimization to automate efficient live visualizations. We demonstrate an end-to-end system implementation, including a web-based IDE (itself built in FORWARD), academic and commercial applications built in FORWARD and a wide variety of JavaScript components supported by the declarative templates.
Pronto
Authors: Xiong Pengcheng , Hacigümüş Hakan
Published: May 2015

Nowadays data analytics applications are accessing more and more data from distributed data stores, creating large amount of data traffic on the network. Therefore, distributed analytic queries are prone to suffer from bad performance in terms of query execution time when they encounter a network resource contention, which is quite common in a shared network. Typical distributed query optimizers do not have a way to solve this problem because historically they have been treating the network underneath as a black-box: they are unable to monitor it, let alone to control it. However, we are entering a new era of software-defined networking (SDN), which provides visibility into and control of the network's state for the applications including distributed database systems. In this demonstration, we present a system, called Pronto that leverages the SDN capabilities for a distributed query processor to achieve performance improvement and differentiation for analytical queries. The system is the real implementation of our recently developed methods on commercial SDN products. The demonstration shows the shortcomings of a distributed query optimizer, which treats the underlying network as a black box, and the advantages of the SDN-based approach by allowing the users to selectively explore various relevant and interesting settings in a distributed query processing environment.
Getting your big data priorities straight
Authors: Zhang Rui , Jain Reshu , Sarkar Prasenjit , Rupprecht Lukas
Published: May 2015

As we come to terms with various big data challenges, one vital issue remains largely untouched. That is the optimal multiplexing and prioritization of different big data applications sharing the same underlying infrastructure, for example, a public cloud platform. Given these demanding applications and the necessary practice to avoid over-provisioning, resource contention between applications is inevitable. Priority must be given to important applications (or sub workloads in an application) in these circumstances.

This demo highlights the compelling impact prioritization could make, using an example application that recommends promising combinations of stocks to purchase based on relevant Twitter sentiment. The application consists of a batch job and an interactive query, ran simultaneously. Our underlying solution provides a unique capability to identify and differentiate application workloads throughout a complex big data platform. Its current implementation is based on Apache Hadoop and the IBM GPFS distributed storage system. The demo showcases the superior interactive query performance achievable by prioritizing its workloads and thereby avoiding I/O bandwidth contention. The query time is 3.6 × better compared to no prioritization. Such a performance is within 0.3% of that of an idealistic system where the query runs without contention. The demo is conducted on around 3 months of Twitter data, pertinent to the S & P 100 index, with about 4 × 10
12
potential stock combinations considered.
SPOT
Authors: Kong Longbo , Liu Zhi , Huang Yan
Published: May 2015

A tremendous amount of information is being shared everyday on social media sites such as Facebook, Twitter or Google+. But only a small portion of users provide their location information, which can be helpful in targeted advertisement and many other services. In this demo we present our large scale user location estimation system, SPOT, which showcase different location estimating models on real world data sets. The demo shows three different location estimation algorithms: a friend-based, a social closeness-based, and an energy and local social coefficient based. The first algorithm is a baseline and the other two new algorithms utilize social closeness information which was traditionally treated as a binary friendship. The two algorithms are based on the premise that friends are different and close friends can help to estimate location better. The demo will also show that all three algorithms benefit from a confidence-based iteration method. The demo is web-based. A user can specify different settings, explore the estimation results on a map, and observe the statistical information, e.g. accuracy and average friends used in the estimation, dynamically. The demo provides two datasets: Twitter (148,860 located users) and Gowalla (99,563 located users). Furthermore, a user can filter users with certain features, e.g. with more than 100 friends, to see how the estimating models work on a particular case. The estimated and real locations of those users as well as their friends will be displayed on the map.
RASP-QS
Authors: Alavi Zohreh , Zhou Lu , Powers James , Chen Keke
Published: May 2015

Hosting data query services in public clouds is an attractive solution for its great scalability and significant cost savings. However, data owners also have concerns on data privacy due to the lost control of the infrastructure. This demonstration shows a prototype for efficient and confidential range/kNN query services built on top of the random space perturbation (RASP) method. The RASP approach provides a privacy guarantee practical to the setting of cloud-based computing, while enabling much faster query processing compared to the encryption-based approach. This demonstration will allow users to more intuitively understand the technical merits of the RASP approach via interactive exploration of the visual interface.
Uncertain entity resolution
Authors: Gal Avigdor
Published: May 2015

Entity resolution is a fundamental problem in data integration dealing with the combination of data from different sources to a unified view of the data. Entity resolution is inherently an uncertain process because the decision to map a set of records to the same entity cannot be made with certainty unless these are identical in all of their attributes or have a common key. In the light of recent advancement in data accumulation, management, and analytics landscape (known as big data) the tutorial re-evaluates the entity resolution process and in particular looks at best ways to handle data veracity. The tutorial ties entity resolution with recent advances in probabilistic database research, focusing on sources of uncertainty in the entity resolution process.
Knowledge bases in the age of big data analytics
Authors: Suchanek Fabian M. , Weikum Gerhard
Published: May 2015

This tutorial gives an overview on state-of-the-art methods for the automatic construction of large knowledge bases and harnessing them for data and text analytics. It covers both big-data methods for building knowledge bases and knowledge bases being assets for big-data applications. The tutorial also points out challenges and research opportunities.
VLDB 2014 Ph.D. workshop
Authors: Li Yunyao , Neuhold Erich
Published: May 2015

The VLDB 2014 PhD Workshop is an one-day event to be held in Hangzhou, China on September 1st, 2014, in conjunction with VLDB 2014. The aim of this workshop is to provide helpful feedback, useful information and networking opportunities that can benefit the students' dissertation work as well as their long-term career. The selection process and the workshop program were carefully designed with this specific goal in mind. The accepted submissions are included in the online proceedings for the Workshop at http://www.vldb.org/2014/phd_workshop_proceedings.html
Engineering high-performance database engines
Authors: Neumann Thomas
Published: May 2015

Developing a database engine is both challenging and rewarding. Database engines are very complex software artifacts that have to scale to large data sizes and large hardware configurations, and developing such systems usually means choosing between different trade-offs at various points of development.

This papers gives a survey over two different database engines, the disk-based SPARQL-processing engine RDF-3X, and the relational main-memory engine HyPer. It discusses the design choices that were made during development, and highlights optimization techniques that are important for both systems.
Realization of the low cost and high performance MySQL cloud database
Authors: Cao Wei , Yu Feng , Xie Jiasen
Published: May 2015

MySQL is a low cost, high performance, good reliability and open source database product, widely used in many Internet companies. For example, there are thousands of MySQL servers being used in Taobao. Although NoSQL developed very quickly in past two years, and new products emerged in endlessly, but in the actual business application of NoSQL, the requirements to developers are relatively high. Moreover, MySQL has many more mature middleware, maintenance tools and a benign ecological circle, so from this perspective, MySQL dominates in the whole situation, while NoSQL is as a supplement. We (the core system database team of Taobao) have done a lot of work in the filed of MySQL hosting platform, designed and implemented a UMP (Unified MySQL Platform) system, to provide a low cost and high performance MySQL cloud database service.
GEMINI
Authors: Ling Zheng Jye , Tran Quoc Trung , Fan Ju , Koh Gerald C. H. , Nguyen Thi , Tan Chuen Seng , Yip James W. L. , Zhang Meihui
Published: May 2015

Healthcare systems around the world are facing the challenge of information overload in caring for patients in an affordable, safe and high-quality manner in a system with limited healthcare resources and increasing costs. To alleviate this problem, we develop an integrative healthcare analytics system called
GEMINI
which allows point of care analytics for doctors where real-time usable and relevant information of their patients are required through the questions they asked about the patients they are caring for.
GEMINI
extracts data of each patient from various data sources and stores them as information in a
patient profile graph.
The data sources are complex and varied consisting of both structured data (such as, patients' demographic data, laboratory results and medications) and unstructured data (such as, doctors' notes). Hence, the patient profile graph provides a holistic and comprehensive information of patients' healthcare profile, from which
GEMINI
can infer implicit information useful for administrative and clinical purposes, and extract relevant information for performing predictive analytics. At the core,
GEMINI
keeps interacting with the healthcare professionals as part of a feedback loop to gather, infer, ascertain and enhance the self-learning knowledge base. We present a case study on using
GEMINI
to predict the risk of unplanned patient readmissions.