bluuebunny/crossref_metadata_2025_split huggingface dataset:
REX
Authors: Fang Lujun , Sarma Anish Das , Yu Cong , Bohannon Philip 
Published: June 2014

Knowledge bases of entities and relations (either constructed manually or automatically) are behind many real world search engines, including those at Yahoo!, Microsoft, and Google. Those knowledge bases can be viewed as graphs with nodes representing entities and edges representing (primary) relationships, and various studies have been conducted on how to leverage them to answer entity seeking queries. Meanwhile, in a complementary direction, analyses over the query logs have enabled researchers to identify entity pairs that are statistically correlated. Such entity relationships are then presented to search users through the "related searches" feature in modern search engines. However, entity relationships thus discovered can often be "puzzling" to the users because why the entities are connected is often indescribable. In this paper, we propose a novel problem calledentity relationship explanation, which seeks to explain why a pair of entities are connected, and solve this challenging problem by integrating the above two complementary approaches, i.e., we leverage the knowledge base to "explain" the connections discovered between entity pairs.More specifically, we presentREX, a system that takes a pair of entities in a given knowledge base as input and efficiently identifies a ranked list of relationship explanations. We formally define relationship explanations and analyze their desirable properties. Furthermore, we design and implement algorithms to efficiently enumerate and rank all relationship explanations based on multiple measures of "interestingness." We perform extensive experiments over real web-scale data gathered from DBpedia and a commercial search engine, demonstrating the efficiency and scalability ofREX. We also perform user studies to corroborate the effectiveness of explanations generated byREX.
Definition, detection, and recovery of single-page failures, a fourth class of database failures
Authors: Graefe Goetz , Kuno Harumi 
Published: June 2014

The three traditional failure classes are system, media, and transaction failures. Sometimes, however, modern storage exhibits failures that differ from all of those. In order to capture and describe such cases, single-page failures are introduced as a fourth failure class. This class encompasses all failures to read a data page correctly and with plausible contents despite all correction attempts in lower system levels.
Efficient recovery seems to require a new data structure called the page recovery index. Its transactional maintenance can be accomplished writing the same number of log records as today's efficient implementations of logging and recovery. Detection and recovery of a single-page failure can be sufficiently fast that the affected data access is merely delayed, without the need to abort the transaction.
SODA
Authors: Blunschi Lukas , Jossen Claudio , Kossmann Donald , Mori Magdalini , Stockinger Kurt 
Published: June 2014

The purpose of data warehouses is to enable business analysts to make better decisions. Over the years the technology has matured and data warehouses have become extremely successful. As a consequence, more and more data has been added to the data warehouses and their schemas have become increasingly complex. These systems still work great in order to generate pre-canned reports. However, with their current complexity, they tend to be a poor match for non tech-savvy business analysts who need answers to ad-hoc queries that were not anticipated.
This paper describes the design, implementation, and experience of the SODA system (Search over DAta Warehouse). SODA bridges the gap between the business needs of analysts and the technical complexity of current data warehouses. SODA enables a Google-like search experience for data warehouses by taking keyword queries of business users and automatically generating executable SQL. The key idea is to use a graph pattern matching algorithm that uses the metadata model of the data warehouse. Our results with real data from a global player in the financial services industry show that SODA produces queries with high precision and recall, and makes it much easier for business users to interactively explore highly-complex data warehouses.
CDAS
Authors: Liu Xuan , Lu Meiyu , Ooi Beng Chin , Shen Yanyan , Wu Sai , Zhang Meihui 
Published: June 2014

Some complex problems, such as image tagging and natural language processing, are very challenging for computers, where even state-of-the-art technology is yet able to provide satisfactory accuracy. Therefore, rather than relying solely on developing new and better algorithms to handle such tasks, we look to the crowdsourcing solution -- employing human participation -- to make good the shortfall in current technology. Crowdsourcing is a good supplement to many computer tasks. A complex job may be divided into computer-oriented tasks and human-oriented tasks, which are then assigned to machines and humans respectively.
To leverage the power of crowdsourcing, we design and implement a Crowdsourcing Data Analytics System, CDAS. CDAS is a framework designed to support the deployment of various crowdsourcing applications. The core part of CDAS is a quality-sensitive answering model, which guides the crowdsourcing engine to process and monitor the human tasks. In this paper, we introduce the principles of our quality-sensitive model. To satisfy user required accuracy, the model guides the crowdsourcing query engine for the design and processing of the corresponding crowdsourcing jobs. It provides an estimated accuracy for each generated result based on the human workers' historical performances. When verifying the quality of the result, the model employs an online strategy to reduce waiting time. To show the effectiveness of the model, we implement and deploy two analytics jobs on CDAS, a twitter sentiment analytics job and an image tagging job. We use real Twitter and Flickr data as our queries respectively. We compare our approaches with state-of-the-art classification and image annotation techniques. The results show that the human-assisted methods can indeed achieve a much higher accuracy. By embedding the quality-sensitive model into crowdsourcing query engine, we effectively reduce the processing cost while maintaining the required query answer quality.
Opening the black boxes in data flow optimization
Authors: Hueske Fabian , Peters Mathias , Sax Matthias J. , Rheinländer Astrid , Bergmann Rico , Krettek Aljoscha , Tzoumas Kostas 
Published: June 2014

Many systems for big data analytics employ a data flow abstraction to define parallel data processing tasks. In this setting, custom operations expressed as user-defined functions are very common. We address the problem of performing data flow optimization at this level of abstraction, where the semantics of operators are not known. Traditionally, query optimization is applied to queries with known algebraic semantics. In this work, we find that a handful of properties, rather than a full algebraic specification, suffice to establish reordering conditions for data processing operators. We show that these properties can be accurately estimated for black box operators by statically analyzing the general-purpose code of their user-defined functions.
We design and implement an optimizer for parallel data flows that does not assume knowledge of semantics or algebraic properties of operators. Our evaluation confirms that the optimizer can apply common rewritings such as selection reordering, bushy join-order enumeration, and limited forms of aggregation push-down, hence yielding similar rewriting power as modern relational DBMS optimizers. Moreover, it can optimize the operator order of nonrelational data flows, a unique feature among today's systems.
Processing a trillion cells per mouse click
Authors: Hall Alexander , Bachmann Olaf , Büssow Robert , Gănceanu Silviu , Nunkesser Marc 
Published: June 2014

Column-oriented database systems have been a real game changer for the industry in recent years. Highly tuned and performant systems have evolved that provide users with the possibility of answering ad hoc queries over large datasets in an interactive manner.
In this paper we present the column-oriented datastore developed as one of the central components of PowerDrill. It combines the advantages of columnar data layout with other known techniques (such as using composite range partitions) and extensive algorithmic engineering on key data structures. The main goal of the latter being to reduce the main memory footprint and to increase the efficiency in processing typical user queries. In this combination we achieve large speed-ups. These enable a highly interactive Web UI where it is common that a single mouse click leads to processing a trillion values in the underlying dataset.
SCOUT
Authors: Tauheed Farhan , Heinis Thomas , Schürmann Felix , Markram Henry , Ailamaki Anastasia 
Published: June 2014

Today's scientists are quickly moving from in vitro to in silico experimentation: they no longer analyze natural phenomena in a petri dish, but instead they build models and simulate them. Managing and analyzing the massive amounts of data involved in simulations is a major task. Yet, they lack the tools to efficiently work with data of this size.
One problem many scientists share is the analysis of the massive spatial models they build. For several types of analysis they need to interactively follow the structures in the spatial model, e.g., the arterial tree, neuron fibers, etc., and issue range queries along the way. Each query takes long to execute, and the total time for executing a sequence of queries significantly delays data analysis. Prefetching the spatial data reduces the response time considerably, but known approaches do not prefetch with high accuracy.

We develop SCOUT, a
structure-aware
method for prefetching data along interactive spatial query sequences. SCOUT uses an approximate graph model of the structures involved in past queries and attempts to identify what particular structure the user follows. Our experiments with neuro-science data show that SCOUT prefetches with an accuracy from 71% to 92%, which translates to a speedup of 4x-15x. SCOUT also improves the prefetching accuracy on datasets from other scientific domains, such as medicine and biology.
ClouDiA
Authors: Zou Tao , Le Bras Ronan , Salles Marcos Vaz , Demers Alan , Gehrke Johannes 
Published: June 2014

An increasing number of distributed data-driven applications are moving into shared public clouds. By sharing resources and operating at scale, public clouds promise higher utilization and lower costs than private clusters. To achieve high utilization, however, cloud providers inevitably allocate virtual machine instances noncontiguously, i.e., instances of a given application may end up in physically distant machines in the cloud. This allocation strategy can lead to large differences in average latency between instances. For a large class of applications, this difference can result in significant performance degradation, unless care is taken in how application components are mapped to instances.
In this paper, we propose ClouDiA, a general deployment advisor that selects application node deployments minimizing either (i) the largest latency between application nodes, or (ii) the longest critical path among all application nodes. ClouDiA employs mixed-integer programming and constraint programming techniques to efficiently search the space of possible mappings of application nodes to instances. Through experiments with synthetic and real applications in Amazon EC2, we show that our techniques yield a 15% to 55% reduction in time-to-solution or service response time, without any need for modifying application code.
XORing elephants
Authors: Sathiamoorthy Maheswaran , Asteris Megasthenis , Papailiopoulos Dimitris , Dimakis Alexandros G. , Vadali Ramkumar , Chen Scott , Borthakur Dhruba 
Published: June 2014

Distributed storage systems for large clusters typically use replication to provide reliability. Recently, erasure codes have been used to reduce the large storage overhead of three-replicated systems. Reed-Solomon codes are the standard design choice and their high repair cost is often considered an unavoidable price to pay for high storage efficiency and high reliability.
This paper shows how to overcome this limitation. We present a novel family of erasure codes that are efficiently repairable and offer higher reliability compared to Reed-Solomon codes. We show analytically that our codes are optimal on a recently identified tradeoff between locality and minimum distance.
We implement our new codes in Hadoop HDFS and compare to a currently deployed HDFS module that uses Reed-Solomon codes. Our modified HDFS implementation shows a reduction of approximately 2× on the repair disk I/O and repair network traffic. The disadvantage of the new coding scheme is that it requires 14% more storage compared to Reed-Solomon codes, an overhead shown to be information theoretically optimal to obtain locality. Because the new codes repair failures faster, this provides higher reliability, which is orders of magnitude higher compared to replication.
Optimization strategies for A/B testing on HADOOP
Authors: Cherniak Andrii , Zaidi Huma , Zadorozhny Vladimir 
Published: June 2014

In this work, we present a set of techniques that considerably improve the performance of executing concurrent MapReduce jobs. Our proposed solution relies on proper resource allocation for concurrent Hive jobs based on data dependency, inter-query optimization and modeling of Hadoop cluster load. To the best of our knowledge, this is the first work towards Hive/MapReduce job optimization which takes Hadoop cluster load into consideration.
We perform an experimental study that demonstrates 233% reduction in execution time for concurrent vs sequential execution schema. We report up to 40% extra reduction in execution time for concurrent job execution after resource usage optimization.
The results reported in this paper were obtained in a pilot project to assess the feasibility of migrating A/B testing from Teradata + SAS analytics infrastructure to Hadoop. This work was performed on eBay production Hadoop cluster.
Making updates disk-I/O friendly using SSDs
Authors: Sadoghi Mohammad , Ross Kenneth A. , Canim Mustafa , Bhattacharjee Bishwaranjan 
Published: June 2014

Multiversion databases store both current and historical data. Rows are typically annotated with timestamps representing the period when the row is/was valid. We develop novel techniques for reducing index maintenance in multiversion databases, so that indexes can be used effectively for analytical queries over current data without being a heavy burden on transaction throughput. To achieve this end, we re-design persistent index data structures in the storage hierarchy to employ an extra level of indirection. The indirection level is stored on solid state disks that can support very fast random I/Os, so that traversing the extra level of indirection incurs a relatively small overhead.
The extra level of indirection dramatically reduces the number of magnetic disk I/Os that are needed for index updates, and localizes maintenance to indexes on updated attributes. Further, we batch insertions within the indirection layer in order to reduce physical disk I/Os for indexing new records. By reducing the index maintenance overhead on transactions, we enable operational data stores to create more indexes to support queries. We have developed a prototype of our indirection proposal by extending the widely used Generalized Search Tree (GiST) open-source project, which is also employed in PostgreSQL. Our working implementation demonstrates that we can significantly reduce index maintenance and/or query processing cost, by a factor of 3. For insertions of new records, our novel batching technique can save up to 90% of the insertion time.
Multi-tuple deletion propagation
Authors: Kimelfeld Benny , Vondrák Jan , Woodruff David P. 
Published: June 2014

This paper studies the computational complexity of the classic problem of deletion propagation in a relational database, where tuples are deleted from the base relations in order to realize a desired deletion of tuples from the view. Such an operation may result in a (sometimes unavoidable) side effect: deletion of additional tuples from the view, besides the intentionally deleted ones. The goal is to minimize the side effect. The complexity of this problem has been well studied in the case where only a single tuple is deleted from the view. However, only little is known within the more realistic scenario of multi-tuple deletion, which is the topic of this paper. The class of conjunctive queries (CQs) is among the most well studied in the literature, and we focus here on views defined by CQs that are self-join free (sjf-CQs).
Our main result is a trichotomy in complexity, classifying all sjf-CQs into three categories: those for which the problem is in polynomial time, those for which the problem is NP-hard but polynomial-time approximable (by a constant-factor), and those for which even an approximation (by any factor) is NP-hard to obtain. A corollary of this trichotomy is a dichotomy in the complexity of deciding whether a side-effect-free solution exists, in the multi-tuple case. We further extend the full classification to accommodate the presence of a constant upper bound on the number of view tuples to delete, and the presence of functional dependencies. Finally, we establish (positive and negative) complexity results on approximability for the dual problem of maximizing the number of view tuples surviving (rather than minimizing the side effect incurred in) the deletion propagation.
DesTeller
Authors: Xue Andy Yuan , Zhang Rui , Zheng Yu , Xie Xing , Yu Jianhui , Tang Yong 
Published: June 2014

Destination prediction is an essential task for a number of emerging location based applications such as recommending sightseeing places and sending targeted advertisements. A common approach to destination prediction is to derive the probability of a location being the destination based on historical trajectories. However, existing techniques suffer from the "data sparsity problem", i.e., the number of available historical trajectories is far from sufficient to cover all possible trajectories. This problem considerably limits the amount of query trajectories whose predicted destinations can be inferred.
In this demonstration, we showcase a system named "DesTeller" that is interactive, user-friendly, publicly accessible, and capable of answering real-time queries. The underlying algorithm Sub-Trajectory Synthesis (SubSyn) successfully addressed the data sparsity problem and is able to predict destinations for almost every query submitted by travellers. We also consider the privacy protection issue in case an adversary uses SubSyn algorithm to derive sensitive location information of users.
Senbazuru
Authors: Chen Zhe , Cafarella Michael , Chen Jun , Prevo Daniel , Zhuang Junfeng 
Published: June 2014

Spreadsheets have become a critical data management tool, but they lack explicit relational metadata, making it difficult to join or integrate data across multiple spreadsheets. Because spreadsheet data are widely available on a huge range of topics, a tool that allows easy spreadsheet integration would be hugely beneficial for a variety of users.We demonstrate that Senbazuru, a prototype spreadsheet database management system (SSDBMS), is able to extract relational information from spreadsheets. By doing so, it opens up opportunities for integration among spreadsheets and with other relational sources. Senbazuru allows users to search for relevant spreadsheets in a large corpus, probabilistically constructs a relational version of the data, and offers several relational operations over the resulting extracted data (including joins to other spreadsheet data). Our demonstration is available on two clients: a JavaScript-rich Web site and a touch interface on the iPad. During the demo, Senbazuru will allow VLDB participants to search spreadsheets, extract relational data from them, and apply relational operators such as select and join.
R2-D2
Authors: Zhou Jingbo , Tung Anthony K. H. , Wu Wei , Ng Wee Siong 
Published: June 2014

Path prediction is presently an important area of research with a wide range of applications. However, most of the existing path prediction solutions are based on eager learning methods which commit to a model or a set of patterns extracted from historical trajectories. Such methods do not perform very well in dynamic environments where the objects' trajectories are affected by many irregular factors which are not captured by pre-defined models or patterns.
In this demonstration, we present the "R2-D2" system that supports probabilistic path prediction in dynamic environments. The core of our system is a "semi-lazy" learning approach to probabilistic path prediction which builds a prediction model on the fly using historical trajectories that are selected dynamically based on the trajectories of target objects. Our "R2-D2" system has a visual interface that shows how our path prediction algorithm works on several real-world datasets. It also allows us to experiment with various parameter settings.
Domain specific multistage query language for medical document repositories
Authors: Madaan Aastha , Bhalla Subhash 
Published: June 2014

Vast amount of medical information is increasingly available on the Web. As a result, seeking medical information through queries is gaining importance in the medical domain. The existing keyword-based search engines such as Google, Yahoo fail to suffice the needs of the health-care workers (who are well-versed with the domain knowledge required for querying) using these they often face results which are irrelevant and not useful for their tasks.
In this paper, we present the need and the challenges for a user-level, domain-specific query language for the specialized document repositories of the medical domain. This topic has not been sufficiently addressed by the existing approaches including SQL-like query languages or general-purpose keyword-based search engines and document-level indexing based search. We aim to bridge the gap between information needs of the skilled/semi-skilled domain users and the query capability provided by the query language. Overcoming such a challenge can facilitate effective use of large volume of information on the Web (and in the electronic health records (EHRs)repositories).
Hardware-oblivious parallelism for in-memory column-stores
Authors: Heimel Max , Saecker Michael , Pirk Holger , Manegold Stefan , Markl Volker 
Published: June 2014

The multi-core architectures of today's computer systems make parallelism a necessity for performance critical applications. Writing such applications in a generic, hardware-oblivious manner is a challenging problem: Current database systems thus rely on labor-intensive and error-prone manual tuning to exploit the full potential of modern parallel hardware architectures like multi-core CPUs and graphics cards. We propose an alternative design for a parallel database engine, based on a single set of hardware-oblivious operators, which are compiled down to the actual hardware at runtime. This design reduces the development overhead for parallel database engines, while achieving competitive performance to hand-tuned systems.
We provide a proof-of-concept for this design by integrating operators written using the parallel programming framework OpenCL into the open-source database MonetDB. Following this approach, we achieve efficient, yet highly portable parallel code without the need for optimization by hand. We evaluated our implementation against MonetDB using TPC-H derived queries and observed a performance that rivals that of MonetDB's query execution on the CPU and surpasses it on the GPU. In addition, we show that the same set of operators runs nearly unchanged on a GPU, demonstrating the feasibility of our approach.