While the use of MapReduce systems (such as Hadoop) for large scale data analysis has been widely recognized and studied, we have recently seen an explosion in the number of systems developed for cloud data serving. These newer systems address "cloud OLTP" applications, though they typically do not support ACID transactions. Examples of systems proposed for cloud serving use include BigTable, PNUTS, Cassandra, HBase, Azure, CouchDB, SimpleDB, Voldemort, and many others. Further, they are being applied to a diverse range of applications that differ considerably from traditional (e.g., TPC-C like) serving workloads. The number of emerging cloud serving systems and the wide range of proposed applications, coupled with a lack of apples-to-apples performance comparisons, makes it difficult to understand the tradeoffs between systems and the workloads for which they are suited. We present the "Yahoo! Cloud Serving Benchmark" (YCSB) framework, with the goal of facilitating performance comparisons of the new generation of cloud data serving systems. We define a core set of benchmarks and report results for four widely used systems: Cassandra, HBase, Yahoo!'s PNUTS, and a simple sharded MySQL implementation. We also hope to foster the development of additional cloud benchmark suites that represent other classes of applications by making our benchmark tool available via open source. In this regard, a key feature of the YCSB framework/tool is that it is extensible--it supports easy definition of new workloads, in addition to making it easy to benchmark new systems.

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Benchmarking cloud serving systems with YCSB

We analyze the nature of the relevance feedback problem in a continuous representation space in the context of content-based image retrieval. Emphasis is put on exploring the uniqueness of the problem and comparing the assumptions, implementations, and merits of various solutions in the literature. An attempt is made to compile a list of critical issues to consider when designing a relevance feedback algorithm. With a comprehensive review as the main portion, this paper also offers some novel solutions and perspectives throughout the discussion.

http://www.ifp.illinois.edu/~xzhou2/Research/papers/Selected_papers/ACM_MSJ.pdf

Relevance feedback in image retrieval: A comprehensive review

While standardization efforts for XML query languages have been progressing, researchers and users increasingly focus on the database technology that has to deliver on the new challenges that the abundance of XML documents poses to data management: validation, performance evaluation and optimization of XML query processors are the upcoming issues. Following a long tradition in database research, we provide a framework to assess the abilities of an XML database to cope with a broad range of different query types typically encountered in real-world scenarios. The benchmark can help both implementors and users to compare XML databases in a standardized application scenario. To this end, we offer a set of queries where each query is intended to challenge a particular aspect of the query processor. The overall workload we propose consists of a scalable document database and a concise, yet comprehensive set of queries which covers the major aspects of XML query processing ranging from textual features to data analysis queries and ad hoc queries. We complement our research with results we obtained from running the benchmark on several XML database platforms. These results are intended to give a first baseline and illustrate the state of the art.

/pdf/xmark-a-benchmark-for-xml-data-management-1poftzlfoc.pdf

XMark: a benchmark for XML data management

Voluminous amounts of data have been produced, since the past decade as the miniaturization of Internet of things (IoT) devices increases. However, such data are not useful without analytic power. Numerous big data, IoT, and analytics solutions have enabled people to obtain valuable insight into large data generated by IoT devices. However, these solutions are still in their infancy, and the domain lacks a comprehensive survey. This paper investigates the state-of-the-art research efforts directed toward big IoT data analytics. The relationship between big data analytics and IoT is explained. Moreover, this paper adds value by proposing a new architecture for big IoT data analytics. Furthermore, big IoT data analytic types, methods, and technologies for big data mining are discussed. Numerous notable use cases are also presented. Several opportunities brought by data analytics in IoT paradigm are then discussed. Finally, open research challenges, such as privacy, big data mining, visualization, and integration, are presented as future research directions.

Big IoT Data Analytics: Architecture, Opportunities, and Open Research Challenges

This paper describes the overall design and architecture of the Timber XML database system currently being implemented at the University of Michigan. The system is based upon a bulk algebra for manipulating trees, and natively stores XML. New access methods have been developed to evaluate queries in the XML context, and new cost estimation and query optimization techniques have also been developed. We present performance numbers to support some of our design decisions. We believe that the key intellectual contribution of this system is a comprehensive set-at-a-time query processing ability in a native XML store, with all the standard components of relational query processing, including algebraic rewriting and a cost-based optimizer.

https://deepblue.lib.umich.edu/bitstream/handle/2027.42/42328/20110274.pdf;sequence=1

TIMBER: A native XML database

In this paper, we present a data and an execution model that allow for efficient storage and retrieval of XML documents in a relational database. The data model is strictly based on the notion of binary associations: by decomposing XML documents into small, flexible and semantically homogeneous units we are able to exploit the performance potential of vertical fragmentation. Moreover, our approach provides clear and intuitive semantics, which facilitates the definition of a declarative query algebra. Our experimental results with large collections of XML documents demonstrate the effectiveness of the techniques proposed.

/pdf/efficient-relational-storage-and-retrieval-of-xml-documents-40iuznhpb8.pdf

Efficient Relational Storage and Retrieval of XML Documents (Extended Version)

While standardized and widely used benchmarks address either operational or real-time Business Intelligence (BI) workloads, the lack of a hybrid benchmark led us to the definition of a new, complex, mixed workload benchmark, called mixed workload CH-benCHmark. This benchmark bridges the gap between the established single-workload suites of TPC-C for OLTP and TPC-H for OLAP, and executes a complex mixed workload: a transactional workload based on the order entry processing of TPC-C and a corresponding TPC-H-equivalent OLAP query suite run in parallel on the same tables in a single database system. As it is derived from these two most widely used TPC benchmarks, the CH-benCHmark produces results highly relevant to both hybrid and classic single-workload systems.

The mixed workload CH-benCHmark

Benchmarks belong to the very standard repertory of tools deployed in database development. Assessing the capabilities of a system, analyzing actual and potential bottlenecks, and, naturally, comparing the pros and cons of different systems architectures have become indispensable tasks as databases management systems grow in complexity and capacity. In the course of the development of XML databases the need for a benchmark framework has become more and more evident: a great many different ways to store XML data have been suggested in the past, each with its genuine advantages, disadvantages and consequences that propagate through the layers of a complex database system and need to be carefully considered. The different storage schemes render the query characteristics of the data variably different. However, no conclusive methodology for assessing these differences is available to date.In this paper, we outline desiderata for a benchmark for XML databases drawing from our own experience of developing an XML repository, involvement in the definition of the standard query language, and experience with standard benchmarks for relational databases.

/pdf/why-and-how-to-benchmark-xml-databases-3oywu8hj2g.pdf

Why and how to benchmark XML databases

Indexed views or materialized views are used as a secondary index on a base table with multi-valued attributes. This provides for using the index to search in the nested data. Moreover, indexing is provided on the result of an unnest operation. Indexing a view on the result of an unnesting operation provides the ability to index the contents of a nested collection. One such unnesting operation is “cross apply unnest”. This provides additional options for a query execution plan, leading to a more optimized query. A back-join is provided from the indexed view to the base table to allow fields from the base table that are not present in the indexed view to be included in a result of a query on the table which is processed using the indexed view as an access path. This provides a means of including columns in the query result that are not in the indexed view but are in the base table. The back-join is supported from a single-table indexed view to the base table via a unique clustering key which acts as a logical row locator. Thus, the system can back-join to the base table from an indexed view via the unique clustering key. These features allow the use of indexed views to index a table on the contents of multi-set or multi-valued attributes.

Florian Waas

Papers

XMark: a benchmark for XML data management

Efficient Relational Storage and Retrieval of XML Documents (Extended Version)

The mixed workload CH-benCHmark

Why and how to benchmark XML databases

Structured indexes on results of function applications over data