The lion's share of bacteria in various environments cannot be cloned in the laboratory and thus cannot be sequenced using existing technologies. A major goal of single-cell genomics is to complement gene-centric metagenomic data with whole-genome assemblies of uncultivated organisms. Assembly of single-cell data is challenging because of highly non-uniform read coverage as well as elevated levels of sequencing errors and chimeric reads. We describe SPAdes, a new assembler for both single-cell and standard (multicell) assembly, and demonstrate that it improves on the recently released E+V-SC assembler (specialized for single-cell data) and on popular assemblers Velvet and SoapDeNovo (for multicell data). SPAdes generates single-cell assemblies, providing information about genomes of uncultivatable bacteria that vastly exceeds what may be obtained via traditional metagenomics studies. SPAdes is available online ( http://bioinf.spbau.ru/spades ). It is distributed as open source software.

SPAdes, a new genome assembly algorithm and its applications to single-cell sequencing ( 7th Annual SFAF Meeting, 2012)

Schema matching is a basic problem in many database application domains, such as data integration, E-business, data warehousing, and semantic query processing. In current implementations, schema matching is typically performed manually, which has significant limitations. On the other hand, previous research papers have proposed many techniques to achieve a partial automation of the match operation for specific application domains. We present a taxonomy that covers many of these existing approaches, and we describe the approaches in some detail. In particular, we distinguish between schema-level and instance-level, element-level and structure-level, and language-based and constraint-based matchers. Based on our classification we review some previous match implementations thereby indicating which part of the solution space they cover. We intend our taxonomy and review of past work to be useful when comparing different approaches to schema matching, when developing a new match algorithm, and when implementing a schema matching component.

/pdf/a-survey-of-approaches-to-automatic-schema-matching-4n2e5qtrpt.pdf

A survey of approaches to automatic schema matching

pdf/optimal-aggregation-algorithms-for-middleware-20o3h48qxy.pdf

Optimal aggregation algorithms for middleware

We classify data quality problems that are addressed by data cleaning and provide an overview of the main solution approaches. Data cleaning is especially required when integrating heterogeneous data sources and should be addressed together with schema-related data transformations. In data warehouses, data cleaning is a major part of the so-called ETL process. We also discuss current tool support for data cleaning.

/pdf/data-cleaning-problems-and-current-approaches-2c16prrxgi.pdf

Data Cleaning: Problems and Current Approaches.

Newness and distinctiveness is claimed in the features of ornamentation as shown inside the broken line circle in the accompanying representation.

Display screen or portion thereof with graphical user interface

Garlic is a middleware system that provides an integrated view of a variety of legacy data sources, without changing how or where data is stored In this paper, we describe our architecture for wrappers, key components of Garlic that encapsulate data sources and mediate between them and the middleware Garlic wrappers model legacy data as objects, participate in query planning, and provide standard interfaces for method invocation and query execution To date, we have built wrappers for 10 data sources Our experience shows that Garlic wrappers can be written quickly and that our architecture is flexible enough to accommodate data sources with a variety of data models and a broad range of traditional and non-traditional query processing capabilities

/pdf/don-t-scrap-it-wrap-it-a-wrapper-architecture-for-legacy-3d3ppzviyw.pdf

Don't Scrap It, Wrap It! A Wrapper Architecture for Legacy Data Sources

Much has been written about the explosion of data, also known as the “data deluge”. Similarly, much of today's research and decision making are based on the de facto acceptance that knowledge and insight can be gained from analyzing and contextualizing the vast (and growing) amount of “open” or “raw” data. The concept that the large number of data sources available today facilitates analyses on combinations of heterogeneous information that would not be achievable via “siloed” data maintained in warehouses is very powerful. The term data lake has been coined to convey the concept of a centralized repository containing virtually inexhaustible amounts of raw (or minimally curated) data that is readily made available anytime to anyone authorized to perform analytical activities. The often unstated premise of a data lake is that it relieves users from dealing with data acquisition and maintenance issues, and guarantees fast access to local, accurate and updated data without incurring development costs (in terms of time and money) typically associated with structured data warehouses. However appealing this premise, practically speaking, it is our experience, and that of our customers, that “raw” data is logistically difficult to obtain, quite challenging to interpret and describe, and tedious to maintain. Furthermore, these challenges multiply as the number of sources grows, thus increasing the need to thoroughly describe and curate the data in order to make it consumable. In this paper, we present and describe some of the challenges inherent in creating, filling, maintaining, and governing a data lake, a set of processes that collectively define the actions of data wrangling, and we propose that what is really needed is a curated data lake, where the lake contents have undergone a curation process that enable its use and deliver the promise of ad-hoc data accessibility to users beyond the enterprise IT staff.

/pdf/data-wrangling-the-challenging-yourney-from-the-wild-to-the-4jdhufrwis.pdf

Data Wrangling: The Challenging Yourney from the Wild to the Lake.

Various embodiments of a method, system and article of manufacture to discover relationships among a first set of elements and a second set of elements are provided. At least one metric algorithm is identified based on a metric selection parameter. A raw result is determined based on the at least one metric algorithm, a first specified structural description of the first set of elements and a second specified structural description of the second set of elements. The raw result comprises a plurality of relationship measurements and the raw result is ordered. In some embodiments, a balanced result is produced based on the raw result and a matching strategy algorithm. In other embodiments, the matching strategy algorithm is identified based on a matching strategy selection parameter.

Tolerant and extensible discovery of relationships in data using structural information and data analysis

Various embodiments of a system and computer program product to access metadata from a plurality of data servers from a federated database management system are provided. In one embodiment, a request for metadata, from a client application, is received by the federated database management system. Data servers which are accessible from the federated database management system are identified. For each data server, metadata describing data of a data source of that data server is retrieved in accordance with the application request. The retrieved metadata from each of the data servers is aggregated to produce an aggregated result in a uniform format. The aggregated result is provided. In another embodiment, for each data server, a source metadata request for metadata of that data server is generated in accordance with the application request and a source metadata application programming interface. A view is created based on the source metadata request for metadata for each data server.

Common interface to access catalog information from heterogeneous databases

The explosion of the Internet and e-business in recent years has caused a secondary explosion in the amounts and types of information available to enterprise applications. Industry analysts predict that more data will be generated in the next three years than in all of recorded history. Because the adoption of Internet-based business transaction models has significantly outpaced the development of tools and technologies to deal with the information explosion, many businesses find their systems breaking under the sheer volume and diversity of data being directed at them. The challenge facing businesses today is information integration. Enterprise applications must interact with databases, application servers, content management systems, data warehouses, workflow systems, search engines, message queues, Web crawlers, mining and analysis packages, and other enterprise integration applications. They must use a variety of programming interfaces and understand a variety of languages and formats. They must extract and combine data in multiple formats generated by multiple delivery mechanisms. Clearly, the boundaries that have traditionally existed between database management systems, content management systems, midtier caches, data warehouses, and other data management systems are blurring, and there is a great need for a platform that provides a unified view of all of these services and the data they deliver.

Information integration: A new generation of information technology

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