Managing and Mining Graph Data is a comprehensive survey book in graph data analytics. It contains extensive surveys on important graph topics such as graph languages, indexing, clustering, data generation, pattern mining, classification, keyword search, pattern matching, and privacy. It also studies a number of domain-specific scenarios such as stream mining, web graphs, social networks, chemical and biological data. The chapters are written by leading researchers, and provide a broad perspective of the area. This is the first comprehensive survey book in the emerging topic of graph data processing. Managing and Mining Graph Data is designed for a varied audience composed of professors, researchers and practitioners in industry. This volume is also suitable as a reference book for advanced-level database students in computer science. About the Editors:Charu C. Aggarwal obtained his B.Tech in Computer Science from IIT Kanpur in 1993 and Ph.D. from MIT in 1996. He has worked as a researcher at IBM since then, and has published over 130 papers in major data mining conferences and journals. He has applied for or been granted over 70 US and International patents, and has thrice been designated a Master Inventor at IBM. He has received an IBM Corporate award for his work on data stream analytics, and an IBM Outstanding Innovation Award for his work on privacy technology. He has served on the executive committees of most major data mining conferences. He has served as an associate editor of the IEEE TKDE, as an associate editor of the ACM SIGKDD Explorations, and as an action editor of the DMKD Journal. He is a fellow of the IEEE, and a life-member of the ACM. Haixun Wang is currently a researcher at Microsoft Research Asia. He received the B.S. and the M.S. degree, both in computer science, from Shanghai Jiao Tong University in 1994 and 1996. He received the Ph.D. degree in computer science from the University of California, Los Angeles in 2000. He subsequently worked as a researcher at IBMuntil 2009. His main research interest is database language and systems, data mining, and information retrieval. He has published more than 100 research papers in referred international journals and conference proceedings. He serves as an associate editor of the IEEE TKDE, and has served as a reviewer and program committee member of leading database conferences and journals.

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Managing and Mining Graph Data

Growing main memory capacity has fueled the development of in-memory big data management and processing. By eliminating disk I/O bottleneck, it is now possible to support interactive data analytics. However, in-memory systems are much more sensitive to other sources of overhead that do not matter in traditional I/O-bounded disk-based systems. Some issues such as fault-tolerance and consistency are also more challenging to handle in in-memory environment. We are witnessing a revolution in the design of database systems that exploits main memory as its data storage layer. Many of these researches have focused along several dimensions: modern CPU and memory hierarchy utilization, time/space efficiency, parallelism, and concurrency control. In this survey, we aim to provide a thorough review of a wide range of in-memory data management and processing proposals and systems, including both data storage systems and data processing frameworks. We also give a comprehensive presentation of important technology in memory management, and some key factors that need to be considered in order to achieve efficient in-memory data management and processing.

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In-Memory Big Data Management and Processing: A Survey

The most acute information management challenges today stem from organizations relying on a large number of diverse, interrelated data sources, but having no means of managing them in a convenient, integrated, or principled fashion. These challenges arise in enterprise and government data management, digital libraries, "smart" homes and personal information management. We have proposed dataspaces as a data management abstraction for these diverse applications and DataSpace Support Platforms (DSSPs) as systems that should be built to provide the required services over dataspaces. Unlike data integration systems, DSSPs do not require full semantic integration of the sources in order to provide useful services. This paper lays out specific technical challenges to realizing DSSPs and ties them to existing work in our field. We focus on query answering in DSSPs, the DSSP's ability to introspect on its content, and the use of human attention to enhance the semantic relationships in a dataspace.

/pdf/principles-of-dataspace-systems-bsrrer6xm3.pdf

Principles of dataspace systems

The World Wide Web is witnessing an increase in the amount of structured content – vast heterogeneous collections of structured data are on the rise due to the Deep Web, annotation schemes like Flickr, and sites like Google Base. While this phenomenon is creating an opportunity for structured data management, dealing with heterogeneity on the web-scale presents many new challenges. In this paper, we highlight these challenges in two scenarios – the Deep Web and Google Base. We contend that traditional data integration techniques are no longer valid in the face of such heterogeneity and scale. We propose a new data integration architecture, PAYGO, which is inspired by the concept of dataspaces and emphasizes pay-as-you-go data management as means for achieving web-scale data integration.

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Web-scale Data Integration: You can only afford to Pay As You Go

The advent of Storage Class Memory (SCM) is driving a rethink of storage systems towards a single-level architecture where memory and storage are merged. In this context, several works have investigated how to design persistent trees in SCM as a fundamental building block for these novel systems. However, these trees are significantly slower than DRAM-based counterparts since trees are latency-sensitive and SCM exhibits higher latencies than DRAM. In this paper we propose a novel hybrid SCM-DRAM persistent and concurrent B-Tree, named Fingerprinting Persistent Tree (FPTree) that achieves similar performance to DRAM-based counterparts. In this novel design, leaf nodes are persisted in SCM while inner nodes are placed in DRAM and rebuilt upon recovery. The FPTree uses Fingerprinting, a technique that limits the expected number of in-leaf probed keys to one. In addition, we propose a hybrid concurrency scheme for the FPTree that is partially based on Hardware Transactional Memory. We conduct a thorough performance evaluation and show that the FPTree outperforms state-of-the-art persistent trees with different SCM latencies by up to a factor of 8.2. Moreover, we show that the FPTree scales very well on a machine with 88 logical cores. Finally, we integrate the evaluated trees in memcached and a prototype database. We show that the FPTree incurs an almost negligible performance overhead over using fully transient data structures, while significantly outperforming other persistent trees.

FPTree: A Hybrid SCM-DRAM Persistent and Concurrent B-Tree for Storage Class Memory

Storage class memory (SCM), a new generation of memory technology, offers non-volatility, high-speed, and byte-addressability, which combines the best properties of current hard disk drives (HDD) and main memory. With these extraordinary features, current systems and software stacks need to be redesigned to get significantly improved performance by eliminating disk input/output (I/O) barriers; and simpler system designs by avoiding complicated data format transformations. In current DBMSs, logging and recovery are the most important components to enforce the atomicity and durability of a database. Traditionally, database systems rely on disks for logging transaction actions and log records are forced to disks when a transaction commits. Because of the slow disk I/O speed, logging becomes one of the major bottlenecks for a DBMS. Exploiting SCM as a persistent memory for transaction logging can significantly reduce logging overhead. In this paper, we present the detailed design of an SCM-based approach for DBMSs logging, which achieves high performance by simplified system design and better concurrency support. We also discuss solutions to tackle several major issues arising during system recovery, including hole detection, partial write detection, and any-point failure recovery. This new logging approach is used to replace the traditional disk based logging approach in DBMSs. To analyze the performance characteristics of our SCM-based logging approach, we implement the prototype on IBM SolidDB. In common circumstances, our experimental results show that the new SCM-based logging approach provides as much as 7 times throughput improvement over disk-based logging in the Telecommunication Application Transaction Processing (TATP) benchmark.

High performance database logging using storage class memory

Decision support and knowledge discovery systems often compute aggregate values of interesting attributes by processing a huge amount of data in very large databases and/or warehouses. In particular, iceberg query is a special type of aggregation query that computes aggregate values above a user-provided threshold. Usually, only a small number of results will satisfy the threshold constraint. Yet, the results often carry very important and valuable business insights. Because of the small result set, iceberg queries offer many opportunities for deep query optimization. However, most existing iceberg query processing algorithms do not take advantage of the small-result-set property and rely heavily on the tuple-scan-based approach. This incurs intensive disk accesses and computation, resulting in long processing time especially when data size is large. Bitmap index, which builds one bitmap vector for each attribute value, is gaining popularity in both column-oriented and row-oriented databases in recent years. It occupies less space than the raw data and gives opportunities for more efficient query processing. In this paper, we exploited the property of bitmap index and developed a very effective bitmap pruning strategy for processing iceberg queries. Our index-pruning-based approach eliminates the need of scanning and processing the entire data set (table) and thus speeds up the iceberg query processing significantly. Experiments show that our approach is much more efficient than existing algorithms commonly used in row-oriented and column-oriented databases.

Efficient Iceberg Query Evaluation Using Compressed Bitmap Index

A computer program product, including: a computer readable storage device to store a computer readable program, wherein the computer readable program, when executed by a processor within a computer, causes the computer to perform operations for logging. The operations include: receiving a transaction including data and a log record corresponding to the data; writing the data to a data storage device; and writing the log record to a log space on a persistent memory device coupled to the data storage device.

Logging system using persistent memory

Database evolution is the process of updating the schema of a database or data warehouse (schema evolution) and evolving the data to the updated schema (data evolution). It is often desired or necessitated when changes occur to the data or the query workload, the initial schema was not carefully designed, or more knowledge of the database is known and a better schema is concluded. The Wikipedia database, for example, has had more than 170 versions in the past 5 years [8]. Unfortunately, although much research has been done on the schema evolution part, data evolution has long been a prohibitively expensive process, which essentially evolves the data by executing SQL queries and re-constructing indexes. This prevents databases from being flexibly and frequently changed based on the need and forces schema designers, who cannot afford mistakes, to be highly cautious. Techniques that enable efficient data evolution will undoubtedly make life much easier.In this paper, we study the efficiency of data evolution, and discuss the techniques for data evolution on column oriented databases, which store each attribute, rather than each tuple, contiguously. We show that column oriented databases have a better potential than traditional row oriented databases for supporting data evolution, and propose a novel data-level data evolution framework on column oriented databases. Our approach, as suggested by experimental evaluations on real and synthetic data, is much more efficient than the query-level data evolution on both row and column oriented databases, which involves unnecessary access of irrelevant data, materializing intermediate results and re-constructing indexes.

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Efficient and scalable data evolution with column oriented databases

In data warehousing and OLAP applications, scalar-level predicates in SQL become increasingly inadequate to support a class of operations that require set-level comparison semantics, i.e., comparing a group of tuples with multiple values. Currently, complex SQL queries composed by scalar-level operations are often formed to obtain even very simple set-level semantics. Such queries are not only difficult to write but also challenging for a database engine to optimize, thus can result in costly evaluation. This paper proposes to augment SQL with set predicate, to bring out otherwise obscured set-level semantics. We studied two approaches to processing set predicates-an aggregate function-based approach and a bitmap index-based approach. Moreover, we designed a histogram-based probabilistic method of set predicate selectivity estimation, for optimizing queries with multiple predicates. The experiments verified its accuracy and effectiveness in optimizing queries.

Set Predicates in SQL: Enabling Set-Level Comparisons for Dynamically Formed Groups

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