bluuebunny/crossref_metadata_2025_split huggingface dataset:
Uncertain time-series similarity
Authors: Dallachiesa Michele , Nushi Besmira , Mirylenka Katsiaryna , Palpanas Themis 
Published: June 2014

In the last years there has been a considerable increase in the availability of continuous sensor measurements in a wide range of application domains, such as Location-Based Services (LBS), medical monitoring systems, manufacturing plants and engineering facilities to ensure efficiency, product quality and safety, hydrologic and geologic observing systems, pollution management, and others.
Due to the inherent imprecision of sensor observations, many investigations have recently turned into querying, mining and storing uncertain data. Uncertainty can also be due to data aggregation, privacy-preserving transforms, and error-prone mining algorithms.
In this study, we survey the techniques that have been proposed specifically for modeling and processing uncertain time series, an important model for temporal data. We provide an analytical evaluation of the alternatives that have been proposed in the literature, highlighting the advantages and disadvantages of each approach, and further compare these alternatives with two additional techniques that were carefully studied before. We conduct an extensive experimental evaluation with 17 real datasets, and discuss some surprising results, which suggest that a fruitful research direction is to take into account the temporal correlations in the time series. Based on our evaluations, we also provide guidelines useful for the practitioners in the field.
Solving big data challenges for enterprise application performance management
Authors: Rabl Tilmann , Gómez-Villamor Sergio , Sadoghi Mohammad , Muntés-Mulero Victor , Jacobsen Hans-Arno , Mankovskii Serge 
Published: June 2014

As the complexity of enterprise systems increases, the need for monitoring and analyzing such systems also grows. A number of companies have built sophisticated monitoring tools that go far beyond simple resource utilization reports. For example, based on instrumentation and specialized APIs, it is now possible to monitor single method invocations and trace individual transactions across geographically distributed systems. This high-level of detail enables more precise forms of analysis and prediction but comes at the price of high data rates (i.e., big data). To maximize the benefit of data monitoring, the data has to be stored for an extended period of time for ulterior analysis. This new wave of big data analytics imposes new challenges especially for the application performance monitoring systems. The monitoring data has to be stored in a system that can sustain the high data rates and at the same time enable an up-to-date view of the underlying infrastructure. With the advent of modern key-value stores, a variety of data storage systems have emerged that are built with a focus on scalability and high data rates as predominant in this monitoring use case.In this work, we present our experience and a comprehensive performance evaluation of six modern (open-source) data stores in the context of application performance monitoring as part of CA Technologies initiative. We evaluated these systems with data and workloads that can be found in application performance monitoring, as well as, on-line advertisement, power monitoring, and many other use cases. We present our insights not only as performance results but also as lessons learned and our experience relating to the setup and configuration complexity of these data stores in an industry setting.
NoDB in action
Authors: Alagiannis Ioannis , Borovica Renata , Branco Miguel , Idreos Stratos , Ailamaki Anastasia 
Published: June 2014

As data collections become larger and larger, users are faced with increasing bottlenecks in their data analysis. More data means more time to prepare the data, to load the data into the database and to execute the desired queries. Many applications already avoid using traditional database systems, e.g., scientific data analysis and social networks, due to their complexity and the increaseddata-to-querytime, i.e. the time between getting the data and retrieving its first useful results. For many applications data collections keep growing fast, even on a daily basis, and thisdata delugewill only increase in the future, where it is expected to have much more data than what we can move or store, let alone analyze.In this demonstration, we will showcase a new philosophy for designing database systems called NoDB. NoDB aims at minimizing the data-to-query time, most prominently by removing the need to load data before launching queries. We will present our prototype implementation, PostgresRaw, built on top of PostgreSQL, which allows for efficient query execution over raw data files with zero initialization overhead. We will visually demonstrate how PostgresRaw incrementally and adaptively touches, parses, caches and indexes raw data files autonomously and exclusively as a side-effect of user queries.
Model-based integration of past & future in TimeTravel
Authors: Khalefa Mohamed E. , Fischer Ulrike , Pedersen Torben Bach , Lehner Wolfgang 
Published: June 2014

We demonstrate TimeTravel, an efficient DBMS system for seamless integrated querying of past and (forecasted) future values of time series, allowing the user to view past and future values as one joint time series. This functionality is important for advanced application domain like energy. The main idea is to compactly represent time series as models. By using models, the TimeTravel system answers queries approximately on past and future data with error guarantees (absolute error and confidence) one order of magnitude faster than when accessing the time series directly. In addition, it efficiently supports exact historical queries by only accessing relevant portions of the time series. This is unlike existing approaches, which access the entire time series to exactly answer the query.To realize this system, we propose a novel hierarchical model index structure. As real-world time series usually exhibits seasonal behavior, models in this index incorporate seasonality. To construct a hierarchical model index, the user specifies seasonality period, error guarantees levels, and a statistical forecast method. As time proceeds, the system incrementally updates the index and utilizes it to answer approximate and exact queries. TimeTravel is implemented into PostgreSQL, thus achieving complete user transparency at the query level. In the demo, we show the easy building of a hierarchical model index for a real-world time series and the effect of varying the error guarantees on the speed up of approximate and exact queries.
InfoPuzzle
Authors: Elmore Aaron J. , Das Sudipto , Agrawal Divyakant , El Abbadi Amr 
Published: June 2014

As Internet-based services and mobile computing devices, such as smartphones and tablets, become ubiquitous, society's reliance on them to accomplish critical and time-sensitive tasks, such as information dissemination and collaborative decision making, also increases. Dependence on these media magnifies the damage caused by their disruption, whether malicious or natural. For instance, a natural disaster disrupting cellular and Internet infrastructures impedes information spread, which in turn leads to chaos, both among the victims as well as the aid providers. Decentralized and ad-hoc mechanisms for information dissemination and decision making are paramount to help restore order.
We demonstrate InfoPuzzle, a mobile peer-to-peer database that utilizes direct device communication to enable group decision making, or consensus, without reliance on centralized communication services. InfoPuzzle minimizes the system's resource consumption, to prolong the lifetime of the power constrained devices by minimizing communication overhead, computational complexity, and persistent storage size. Due to user mobility and the limited range of point-to-point communication, knowing the exact number of participants is impossible, and therefore traditional consensus or quorum protocols cannot be used. We rely of distinct counting techniques, probabilistic thresholds, and bounded time based approaches to reach agreement. In this demo, we will explore various challenges and heuristics in estimating group participation to aid users in reconciling consensus without centralized services.
Graph synopses, sketches, and streams
Authors: Guha Sudipto , McGregor Andrew 
Published: June 2014

Massive graphs arise in any application where there is data about both basic entities and the relationships between these entities, e.g., web-pages and hyperlinks; neurons and synapses; papers and citations; IP addresses and network flows; people and their friendships. Graphs have also become the de facto standard for representing many types of highly structured data. However, the sheer size of many of these graphs renders classical algorithms inapplicable when it comes to analyzing such graphs. In addition, these existing algorithms are typically ill-suited to processing distributed or stream data.
Various platforms have been developed for processing large data sets. At the same time, there is the need to develop new algorithmic ideas and paradigms. In the case of graph processing, a lot of recent work has focused on understanding the important algorithmic issues. An central aspect of this is the question of how to construct and leverage small-space synopses in graph processing. The goal of this tutorial is to survey recent work on this question and highlight interesting directions for future research.
Computing immutable regions for subspace top-k queries
Authors: Mouratidis Kyriakos , Pang HweeHwa 
Published: June 2014

Given a high-dimensional dataset, a top-kquery can be used to shortlist thektuples that best match the user's preferences. Typically, these preferences regard a subset of the available dimensions (i.e., attributes) whose relative significance is expressed by user-specified weights. Along with the query result, we propose to compute for each involved dimension the maximal deviation to the corresponding weight for which the query result remains valid. The derived weight ranges, called immutable regions, are useful for performing sensitivity analysis, for finetuning the query weights, etc.In this paper, we focus on top-kqueries with linear preference functions over the queried dimensions. We codify the conditions under which changes in a dimension's weight invalidate the query result, and develop algorithms to compute the immutable regions. In general, this entails the examination of numerous non-result tuples. To reduce processing time, we introduce a pruning technique and a thresholding mechanism that allow the immutable regions to be determined correctly after examining only a small number of non-result tuples. We demonstrate empirically that the two techniques combine well to form a robust and highly resource-efficient algorithm. We verify the generality of our findings using real high-dimensional data from different domains (documents, images, etc) and with different characteristics.
RACE
Authors: Mansour Essam , El-Roby Ahmed , Kalnis Panos , Ahmadia Aron , Aboulnaga Ashraf 
Published: June 2014

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

It is quite common for networks emerging nowadays to have labels or textual contents on the nodes. On such networks, we study the problem of top-knearest keyword (k-NK) search. In a networkGmodeled as an undirected graph, each node is attached with zero or more keywords, and each edge is assigned with a weight measuring its length. Given a query nodeqinGand a keyword λ, a k-NK query seeksknodes which contain λ and are nearest toq. k-NK is not only useful as a stand-alone query but also as a building block for tackling complex graph pattern matching problems.The key to an accurate k-NK result is a precise shortest distance estimation in a graph. Based on the latest distance oracle technique, we build a shortest path tree for a distance oracle and use the tree distance as a more accurate estimation. With such representation, the original k-NK query on a graph can be reduced to answering the query on a set of trees and then assembling the results obtained from the trees. We propose two efficient algorithms to report the exact k-NK result on a tree. One is query time optimized for a scenario when a small number of result nodes are of interest to users. The other handles k-NK queries for an arbitrarily largekefficiently. In obtaining a k-NK result on a graph from that on trees, a global storage technique is proposed to further reduce the index size and the query time. Extensive experimental results conform with our theoretical findings, and demonstrate the effectiveness and efficiency of our k-NK algorithms on large real graphs.
MillWheel
Authors: Akidau Tyler , Balikov Alex , Bekiroğlu Kaya , Chernyak Slava , Haberman Josh , Lax Reuven , McVeety Sam , Mills Daniel , Nordstrom Paul , Whittle Sam 
Published: June 2014

MillWheel is a framework for building low-latency data-processing applications that is widely used at Google. Users specify a directed computation graph and application code for individual nodes, and the system manages persistent state and the continuous flow of records, all within the envelope of the framework's fault-tolerance guarantees.
This paper describes MillWheel's programming model as well as its implementation. The case study of a continuous anomaly detector in use at Google serves to motivate how many of MillWheel's features are used. MillWheel's programming model provides a notion of logical time, making it simple to write time-based aggregations. MillWheel was designed from the outset with fault tolerance and scalability in mind. In practice, we find that MillWheel's unique combination of scalability, fault tolerance, and a versatile programming model lends itself to a wide variety of problems at Google.
Scuba
Authors: Abraham Lior , Allen John , Barykin Oleksandr , Borkar Vinayak , Chopra Bhuwan , Gerea Ciprian , Merl Daniel , Metzler Josh , Reiss David , Subramanian Subbu , Wiener Janet L. , Zed Okay 
Published: June 2014

Facebook takes performance monitoring seriously. Performance issues can impact over one billion users so we track thousands of servers, hundreds of PB of daily network traffic, hundreds of daily code changes, and many other metrics. We require latencies of under a minute from events occuring (a client request on a phone, a bug report filed, a code change checked in) to graphs showing those events on developers' monitors.
Scuba is the data management system Facebook uses for most real-time analysis. Scuba is a fast, scalable, distributed, in-memory database built at Facebook. It currently ingests millions of rows (events) per second and expires data at the same rate. Scuba stores data completely in memory on hundreds of servers each with 144 GB RAM. To process each query, Scuba aggregates data from all servers. Scuba processes almost a million queries per day. Scuba is used extensively for interactive, ad hoc, analysis queries that run in under a second over live data. In addition, Scuba is the workhorse behind Facebook's code regression analysis, bug report monitoring, ads revenue monitoring, and performance debugging.
Context-aware computing
Authors: Chang Edward Y. 
Published: June 2014

A 2011 Gartner report [3] describes context-aware computing as a game-changing opportunity for enterprises to improve both productivity and profits. Context-aware computing is about making applications and content more relevant to a user's context, e.g., when and where the user is, thereby improving user experience. For instance, a coupon delivered to a user at a wrong time or at a wrong location is considered a nuisance. On the contrary, receiving a timely, usable coupon before purchasing a merchandise is a treat. Context-aware computing is not a new concept, but the ongoing mobile revolution makes it both necessary and feasible. • Necessary because the mobile phone display is small and information must be delivered with much higher relevance and precision to meet user needs. • Feasible because small, light-weight mobile devices allow users to almost always carry them around, and much can be learned via a phone about its user's habits and states.
Context-aware computing involves first acquiring context and then taking context-dependent actions. For instance, a phone can sense a user's location and turn off its GPS unit to conserve power when the user enters a building, or it can collect EKG signals of a user and trigger an alert if the user's heart beats irregularly. Similarly, a restaurant can send a coupon to a user when that user is queued up in front of a nearby restaurant. The useful context can be divided into three categories: information on the user (knowledge of habits, emotional state, biophysiological conditions), the user's environment (time, location, co-location of others, social interaction), and the user's tasks (transportation mode, engaged tasks, general goals) [4]. Context-aware computing can be applied to benefit applications in many areas including but not limited to information retrieval, facility management, productivity enhancement, in addition to the aforementioned three examples representing power management, health care, and commerce, respectively.
Toward scalable transaction processing
Authors: Ailamaki Anastasia , Johnson Ryan , Pandis Ippokratis , Tözün Pínar 
Published: June 2014

Designing scalable transaction processing systems on modern multicore hardware has been a challenge for almost a decade. The typical characteristics of transaction processing workloads lead to a high degree of unbounded communication on multicores for conventional system designs.
In this tutorial, we initially present a systematic way of eliminating scalability bottlenecks of a transaction processing system, which is based on minimizing the unbounded communication. Then, we show several techniques that apply the presented methodology to minimize logging, locking, latching etc. related bottlenecks of transaction processing systems. In parallel, we demonstrate the internals of the Shore-MT storage manager and how they have evolved over the years in terms of scalability on multicore hardware through such techniques. We also teach how to use Shore-MT with the various design options it offers through its application layer Shore-Kits and Metadata Frontend.
RCSI
Authors: Wandelt Sebastian , Starlinger Johannes , Bux Marc , Leser Ulf 
Published: June 2014

Until recently, genomics has concentrated on comparing sequences between species. However, due to the sharply falling cost of sequencing technology, studies of populations of individuals of the same species are now feasible and promise advances in areas such as personalized medicine and treatment of genetic diseases. A core operation in such studies is read mapping, i.e., finding all parts of a set of genomes which are within edit distancekto a given query sequence (k-approximate search). To achieve sufficient speed, current algorithms solve this problem only for one to-be-searched genome and compute only approximate solutions, i.e., they miss somek- approximate occurrences.We present RCSI, Referentially Compressed Search Index, which scales to a thousand genomes and computes the exact answer. It exploits the fact that genomes of different individuals of the same species are highly similar by first compressing the to-be-searched genomes with respect to a reference genome. Given a query, RCSI then searches the reference and all genome-specific individual differences. We propose efficient data structures for representing compressed genomes and present algorithms for scalable compression and similarity search. We evaluate our algorithms on a set of 1092 human genomes, which amount to approx. 3 TB of raw data. RCSI compresses this set by a ratio of 450:1 (26:1 including the search index) and answers similarity queries on a mid-class server in 15 ms on average even for comparably large error thresholds, thereby significantly outperforming other methods. Furthermore, we present a fast and adaptive heuristic for choosing the best reference sequence for referential compression, a problem that was never studied before at this scale.
Ringtail
Authors: Antenucci Dolan , Li Erdong , Liu Shaobo , Zhang Bochun , Cafarella Michael J. , Ré Christopher 
Published: June 2014

Social media nowcasting--using online user activity to describe real-world phenomena--is an active area of research to supplement more traditional and costly data collection methods such as phone surveys. Given the potential impact of such research, we would expect general-purpose nowcasting systems to quickly become a standard tool among noncomputer scientists, yet it has largely remained a research topic. We believe a major obstacle to widespread adoption is the nowcasting feature selection problem. Typical nowcasting systems require the user to choose a handful of social media objects from a pool of billions of potential candidates, which can be a time-consuming and error-prone process.
We have built RINGTAIL, a nowcasting system that helps the user by automatically suggesting high-quality signals. We demonstrate that RINGTALL can make nowcasting easier by suggesting relevant features for a range of topics. The user provides just a short topic query (e.g., unemployment) and a small conventional dataset in order for RINGTALL to quickly return a usable predictive nowcasting model.