bluuebunny/crossref_metadata_2025_split huggingface dataset:
Sacralité épique ou profanation fabuleuse? L’éducation séculière de L’honneur de la tribu de Rachid Mimouni
Authors: Vallury Raji 
Published: June 2014

Abstract
The epic and the novel are narrative forms closely tied to the modern experience of the secular, or what George Lukács has described as a world abandoned by God. This article examines whether the postcolonial novel produced at the margins of the European literary tradition stands in a similar aesthetico-political relationship to the experience of secular modernity. Using Algeria as a case study, it reviews the historical, political, and cultural tensions between secularism and Islamism in postcolonial Algeria. Next, it analyses Rachid Mimouni’s L’honneur de la tribu, a novel published in 1989, the watershed year that inaugurated the bloody civil war between the secular, socialist state and the Islamist opposition in Algeria. Borrowing elements from the epic and the fable, the novel’s dystopic imaginary blurs the distinction between the sacred and the profane in order to express a secularism of pluralism and the toleration of difference.
Quelle est la relation entre la sécularité, le sécularisme et le projet pédagogique du roman? Qu’est-ce que la littérature pourrait nous apprendre dans un monde privé des certitudes de la transcendance et livré à la contingence de son immanence? ‘Le roman’, déclare Lukács, ‘est l’épopée d’un monde abandonné par Dieu’. Notre article propose une lecture d’un roman par un écrivain algérien postcolonial, L’honneur de la tribu de Rachid Mimouni, afin de tenter de répondre à ces interrogations. Le roman a été publié en 1989, l’année qui a inauguré la guerre civile sanglante en Algérie. L’honneur de la tribu, qui affiche toutes les caractéristiques littéraires de l’épopée, de la fable, de la satire et de l’utopie, constitue un roman d’apprentissage ou un roman d’éducation, où la narration se déploie par le biais d’une écriture parabolique. Mais son impulsion ouvertement didactique se fourvoie dans les détours inventifs d’un langage figuré qui contourne la fixité programmatique d’un destin religieux pour la nation et la communauté politique. L’ambiguïté ironique, en déviant les bases d’une instrumentalisation de la foi religieuse, fonctionne comme le médiateur évanouissant qui sécularise le sécularisme à travers un engagement autocritique avec la justice sociale, l’égalité, le pluralisme et la tolérance.
Web page language identification based on URLs
Authors: Baykan Eda , Henzinger Monika , Weber Ingmar 
Published: June 2014

Given
only
the URL of a web page, can we identify its language? This is the question that we examine in this paper.

Such a language classifier is, for example, useful for crawlers of web search engines, which frequently try to satisfy certain language quotas. To determine the language of uncrawled web pages, they have to download the page, which might be wasteful, if the page is not in the desired language. With URL-based language classifiers these redundant downloads can be avoided.
We apply a variety of machine learning algorithms to the language identification task and evaluate their performance in extensive experiments for five languages: English, French, German, Spanish and Italian. Our best methods achieve an F-measure, averaged over all languages, of around .90 for both a random sample of 1,260 web page from a large web crawl and for 25k pages from the ODP directory. For 5k pages of web search engine results we even achieve an F-measure of .96. The achieved recall for these collections is .93, .88 and .95 respectively. Two independent human evaluators performed considerably worse on the task, with an F-measure of .75 and a typical recall of a mere .67. Using only country-code top-level domains, such as .de or .fr yields a good precision, but a typical recall of below .60 and an F-measure of around .68.
Access control over uncertain data
Authors: Rastogi Vibhor , Suciu Dan , Welbourne Evan 
Published: June 2014

Access control is the problem of regulating access to secret information based on certain context information. In traditional applications, context information is known exactly, permitting a simple allow/deny semantics. In this paper, we look at access control when the context is itself uncertain. Our motivating application is RFID data management, in which the location of objects and people, and the associations between them is often uncertain to the system, yet access to private data is strictly defined in terms of these locations and associations.
We formalize a natural semantics for access control that allows the release of partial information in the presence of uncertainty and describe an algorithm that uses a provably optimal perturbation function to enforce these semantics. To specify access control policies in practice, we describe UCAL, a new access control language for uncertain data. We then describe an output perturbation algorithm to implement access control policies described by UCAL. We carry out a set of experiments that demonstrate the feasibility of our approach and confirm its superiority over other possible approaches such as thresholding or sampling.
Scheduling shared scans of large data files
Authors: Agrawal Parag , Kifer Daniel , Olston Christopher 
Published: June 2014

We study how best to schedule scans of large data files, in the presence of many simultaneous requests to a common set of files. The objective is to maximize the overall rate of processing these files, by sharing scans of the same file as aggressively as possible, without imposing undue wait time on individual jobs. This scheduling problem arises in batch data processing environments such as Map-Reduce systems, some of which handle tens of thousands of processing requests daily, over a shared set of files.
As we demonstrate, conventional scheduling techniques such as shortest-job-first do not perform well in the presence of cross-job sharing opportunities. We derive a new family of scheduling policies specifically targeted to sharable workloads. Our scheduling policies revolve around the notion that, all else being equal, it is good to schedule nonsharable scans ahead of ones that can share IO work with future jobs, if the arrival rate of sharable future jobs is expected to be high. We evaluate our policies via simulation over varied synthetic and real workloads, and demonstrate significant performance gains compared with conventional scheduling approaches.
Discovering data quality rules
Authors: Chiang Fei , Miller Renée J. 
Published: June 2014

Dirty data is a serious problem for businesses leading to incorrect decision making, inefficient daily operations, and ultimately wasting both time and money. Dirty data often arises when domain constraints and business rules, meant to preserve data consistency and accuracy, are enforced incompletely or not at all in application code.
In this work, we propose a new data-driven tool that can be used within an organization's data quality management process to suggest possible rules, and to identify conformant and non-conformant records. Data quality rules are known to be contextual, so we focus on the discovery of context-dependent rules. Specifically, we search for conditional functional dependencies (CFDs), that is, functional dependencies that hold only over a portion of the data. The output of our tool is a set of functional dependencies together with the context in which they hold (for example, a rule that states for CS graduate courses, the course number and term functionally determines the room and instructor). Since the input to our tool will likely be a dirty database, we also search for CFDs that almost hold. We return these rules together with the non-conformant records (as these are potentially dirty records).
We present effective algorithms for discovering CFDs and dirty values in a data instance. Our discovery algorithm searches for minimal CFDs among the data values and prunes redundant candidates. No universal objective measures of data quality or data quality rules are known. Hence, to avoid returning an unnecessarily large number of CFDs and only those that are most interesting, we evaluate a set of interest metrics and present comparative results using real datasets. We also present an experimental study showing the scalability of our techniques.
Oracle Database Replay
Authors: Colle Romain , Galanis Leonidas , Buranawatanachoke Supiti , Papadomanolakis Stratos , Wang Yujun 
Published: June 2014

This demonstration presents Oracle Database Replay, a novel approach to testing changes to the relational database management system component of an information system (software upgrades, hardware changes etc). Database Replay makes it possible to subject a test system to a real production workload, which helps identify all potential problems before implementing the planned changes on the production system. Any interesting workload period of a production database system can be captured with minimal overhead. The captured workload is used to drive a test system while maintaining the concurrency and load characteristics of the real production workload.
The demonstration showcases how important maintaining the concurrency and load characteristics of the real workload is. The current testing solutions do not allow for synchronization based on data dependencies. Without proper synchronization the demonstration workload does not perform the work required and does not exercise the test system appropriately, leading to poor coverage and inadequate load. Thus many issues remain undetected. Database replay with its data based synchronization makes testing realistic and leads to the discovery of potential problems.
How best to build web-scale data managers?
Authors: Bernstein Philip A. , Abadi Daniel J. , Cafarella Michael J. , Hellerstein Joseph M. , Kossmann Donald , Madden Samuel 
Published: June 2014

Many of the largest database-driven web sites use custom web-scale data managers (WDMs). On the surface, these WDMs are being applied to problems that are well-suited for relational database systems. Some examples are the following:
• Map-Reduce [5], Hadoop [7], and Dryad [9] are used to process queries on large data sets using sequential scan and aggregation. Hive [8] is a data warehouse built on Hadoop.
• Google's Bigtable [3] is used to store a replicated table of rows of semi-structured data.
• Amazon's Dynamo [6] is used to store partitioned, replicated databases of key-value pairs. Cassandra [2] is similar.
• Object caching systems are used instead of a persistent store, such as memcached [10], Oracle's Coherence, and Microsoft's Velocity project.
On-the-fly progress detection in iterative stream queries
Authors: Chandramouli Badrish , Goldstein Jonathan , Maier David 
Published: June 2014

Multiple researchers have proposed cyclic query plans for evaluating iterative queries over streams or rapidly changing input. The Declarative Networking community uses cyclic plans to evaluate Datalog programs that track reachability and other graph traversals on networks. Cyclic query plans can also evaluate pattern-matching and other queries based on event sequences.
An issue with cyclic queries over dynamic inputs is knowing when the query result has progressed to a certain point in the input, since the number of iterations is data dependent. One option is a "strictly staged" computation, where the query plan quiesces between inputs. This option introduces significant latency, and may also "underload" inter-operator buffers. An alternative is to settle for soft guarantees, such as "eventual consistency". Such imprecision can make it difficult, for example, to know when to purge state from stateful operators.

We propose a third option in which cyclic queries run continuously, but detect progress "on the fly" by means of a
Flying Fixed-Point
(FFP) operator. FFP sits on the cyclic loop and circulates speculative predictions on forward progress, which it then validates. FFP is always able to track progress for a class of queries we term
strongly convergent
. A key advantage of FFP is that it works with existing algebra operators, thereby inheriting their capabilities, such as windowing and dealing with out-of-order input. Also, for stream systems that explicitly model input-event lifetimes, we know exactly which values are in the query result at each point in time.

A key implementation decision is the method for speculating. Using the high-water mark of data events minimizes the number of speculative punctuations. Probing operators on the cyclic loop to determine their external progress circulates many more speculative messages, but tracks actual output progress more closely. We show how a hybrid approach limits predictions while coming close the progress-tracking ability of Probing.
Anticipatory DTW for efficient similarity search in time series databases
Authors: Assent Ira , Wichterich Marc , Krieger Ralph , Kremer Hardy , Seidl Thomas 
Published: June 2014

Time series arise in many different applications in the form of sensor data, stocks data, videos, and other time-related information. Analysis of this data typically requires searching for similar time series in a database. Dynamic Time Warping (DTW) is a widely used high-quality distance measure for time series. As DTW is computationally expensive, efficient algorithms for fast computation are crucial.
In this paper, we propose a novel filter-and-refine DTW algorithm called Anticipatory DTW. Existing algorithms aim at efficiently finding similar time series by filtering the database and computing the DTW in the refinement step. Unlike these algorithms, our approach exploits previously unused information from the filter step during the refinement, allowing for faster rejection of false candidates. We characterize a class of applicable filters for our approach, which comprises state-of-the-art lower bounds of the DTW.
Our novel anticipatory pruning incurs hardly any over-head and no false dismissals. We demonstrate substantial efficiency improvements in thorough experiments on synthetic and real world time series databases and show that our technique is highly scalable to multivariate, long time series and wide DTW bands.
Consistent histograms in the presence of distinct value counts
Authors: Kaushik Raghav , Suciu Dan 
Published: June 2014

Self-tuning histograms have been proposed in the past as an attempt to leverage feedback from query execution. However, the focus thus far has been on histograms that only store cardinalities. In this paper, we study consistent histogram construction from query feedback that also takes distinct value counts into account.
We first show how the entropy maximization (EM) principle can be leveraged to identify a distribution that approximates the data given the execution feedback making the least additional assumptions. This EM model that takes both distinct value counts and cardinalities into account. However, we find that it is computationally prohibitively expensive.

We thus consider an alternative formulation for consistency -- for a given query workload, the goal is to minimize the
L
2
distance between the true and estimated cardinalities. This approach also handles both cardinalities and distinct values counts. We propose an efficient one-pass algorithm with several theoretical properties modeling this formulation. Our experiments show that this approach produces similar improvements in accuracy as the EM based approach while being computationally significantly more efficient.
Index interactions in physical design tuning
Authors: Schnaitter Karl , Polyzotis Neoklis , Getoor Lise 
Published: June 2014

One of the key tasks of a database administrator is to optimize the set of materialized indices with respect to the current workload. To aid administrators in this challenging task, commercial DBMSs provide advisors that recommend a set of indices based on a sample workload. It is left for the administrator to decide which of the recommended indices to materialize and when. This decision requires some knowledge of how the indices benefit the workload, which may be difficult to understand if there are any dependencies or interactions among indices. Unfortunately, advisors do not provide this crucial information as part of the recommendation.
Motivated by this shortcoming, we propose a framework and associated tools that can help an administrator understand the interactions within the recommended set of indices. We formalize the notion of index interactions and develop a novel algorithm to identify the interaction relationships that exist within a set of indices. We present experimental results with a prototype implementation over IBM DB2 that demonstrate the efficiency of our approach. We also describe two new database tuning tools that utilize information about index interactions. The first tool visualizes interactions based on a partitioning of the index-set into non-interacting subsets, and the second tool computes a schedule that materializes the indices over several maintenance windows with maximal overall benefit. In both cases, we provide strong analytical results showing that index interactions can enable enhanced functionality.
Enabling real time data analysis
Authors: Srivastava Divesh , Golab Lukasz , Greer Rick , Johnson Theodore , Seidel Joseph , Shkapenyuk Vladislav , Spatscheck Oliver , Yates Jennifer 
Published: June 2014

Network-based services have become a ubiquitous part of our lives, to the point where individuals and businesses have often come to critically rely on them. Building and maintaining such reliable, high performance network and service infrastructures requires the ability to rapidly investigate and resolve complex service and performance impacting issues. To achieve this, it is important to collect, correlate and analyze massive amounts of data from a diverse collection of data sources in real time.
We have designed and implemented a variety of data systems at AT&T Labs-Research to build highly scalable databases that support real time data collection, correlation and analysis, including (a) the Daytona data management system, (b) the DataDepot data warehousing system, (c) the GS tool data stream management system, and (d) the Bistro data feed manager. Together, these data systems have enabled the creation and maintenance of a data warehouse and data analysis infrastructure for troubleshooting complex issues in the network. We describe these data systems and their key research contributions in this paper.
x-RDF-3X
Authors: Neumann Thomas , Weikum Gerhard 
Published: June 2014

The RDF data model is gaining importance for applications in computational biology, knowledge sharing, and social communities. Recent work on RDF engines has focused on scalable performance for querying, and has largely disregarded updates. In addition to incremental bulk loading, applications also require online updates with flexible control over multi-user isolation levels and data consistency. The challenge lies in meeting these requirements while retaining the capability for fast querying.
This paper presents a comprehensive solution that is based on an extended deferred-indexing method with integrated versioning. The version store enables time-travel queries that are efficiently processed without adversely affecting queries on the current data. For flexible consistency, transactional concurrency control is provided with options for either snapshot isolation or full serializability. All methods are integrated in an extension of the RDF-3X system, and their very good performance for both queries and updates is demonstrated by measurements of multi-user workloads with real-life data as well as stress-test synthetic loads.
Searching workflows with hierarchical views
Authors: Liu Ziyang , Shao Qihong , Chen Yi 
Published: June 2014

Workflows are prevalent in diverse applications, which can be scientific experiments, business processes, web services, or recipes. With the dramatically growing number of workflows, there is an increasing need for people to search a workflow repository using keywords and to retrieve the relevant ones. A workflow hierarchy is a three dimensional object containing multiple abstraction views of different granularity on the same workflow. This unique structure poses a new set of challenges compared to keyword search on tree or graph structures typically found in relational or XML data.
In this paper, we define an informative, self-contained and concise search result on workflows to be a projection of a workflow hierarchy on a two dimensional viewing plane inferred from user queries. We then design and develop an efficient keyword search engine for workflows. Experimental evaluation demonstrates the effectiveness of our approach.
Mining significant semantic locations from GPS data
Authors: Cao Xin , Cong Gao , Jensen Christian S. 
Published: June 2014

With the increasing deployment and use of GPS-enabled devices, massive amounts of GPS data are becoming available. We propose a general framework for the mining of semantically meaningful, significant locations, e.g., shopping malls and restaurants, from such data.
We present techniques capable of extracting semantic locations from GPS data. We capture the relationships between locations and between locations and users with a graph. Significance is then assigned to locations using random walks over the graph that propagates significance among the locations. In doing so, mutual reinforcement between location significance and user authority is exploited for determining significance, as are aspects such as the number of visits to a location, the durations of the visits, and the distances users travel to reach locations. Studies using up to 100 million GPS records from a confined spatio-temporal region demonstrate that the proposal is effective and is capable of outperforming baseline methods and an extension of an existing proposal.
NET-FLi
Authors: Fusco Francesco , Stoecklin Marc Ph. , Vlachos Michail 
Published: June 2014

The ever-increasing number of intrusions in public and commercial networks has created the need for high-speed archival solutions that continuously store streaming network data to enable forensic analysis and auditing. However, "turning back the clock" for post-attack analyses is not a trivial task. The first major challenge is that the solution has to sustain data archiving under extremely high-speed insertion rates. Moreover, the archives created need to be stored in a format that is compressed but still amenable to indexing. The above requirements make general-purpose databases unsuitable for this task, and, thus, dedicated solutions are required.
In this paper, we describe a prototype solution that satisfies all requirements for high-speed archival storage, indexing and data querying on network flow information. The superior performance of our approach is attributed to the on-the-fly compression and indexing scheme, which is based on compressed bitmap principles. Typical commercial solutions can currently process 20,000--60,000 flows per second. An evaluation of our prototype implementation on current commodity hardware using real-world traffic traces shows its ability to sustain insertion rates ranging from 500,000 to more than 1 million records per second. The system offers interactive query response times that enable administrators to perform complex analysis tasks on-the-fly. Our technique is directly amenable to parallel execution, allowing its application in domains that are challenged by large volumes of historical measurement data, such as network auditing, traffic behavior analysis and large-scale data visualization in service provider networks.