The work on large-scale graph analytics to date has largely focused on the study of static properties of graph snapshots. However, a static view of interactions between entities is often an oversimplification of several complex phenomena like the spread of epidemics, information diffusion, formation of online communities , and so on. Being able to find temporal interaction patterns, visualize the evolution of graph properties, or even simply compare them across time, adds significant value in reasoning over graphs. However, because of lack of underlying data management support, an analyst today has to manually navigate the added temporal complexity of dealing with large evolving graphs. In this paper, we present a system, called Historical Graph Store, that enables users to store large volumes of historical graph data and to express and run complex temporal graph analytical tasks against that data. It consists of two key components: a Temporal Graph Index (TGI), that compactly stores large volumes of historical graph evolution data in a partitioned and distributed fashion; it provides support for retrieving snapshots of the graph as of any timepoint in the past or evolution histories of individual nodes or neighborhoods; and a Spark-based Temporal Graph Analysis Framework (TAF), for expressing complex temporal analytical tasks and for executing them in an efficient and scalable manner. Our experiments demonstrate our system's efficient storage, retrieval and analytics across a wide variety of queries on large volumes of historical graph data.
Temporal data management for relational databases was a topic of active research in the 80s and early 90s. Snapshot index @cite_47 is an I O optimal solution to the problem of snapshot retrieval for transaction-time databases. Salzberg and Tsotras @cite_35 present a comprehensive survey of temporal data indexing techinques, and discuss two extreme approaches to supporting snapshot retrieval queries, referred to as the and approaches. While the copy approach relies on storing new copies of a snapshot upon every point of change in the database, the log approach relies on storing everything through changes. Their hybrid is often referred to as the approach. We omit a detailed discussion of the work on temporal databases, and refer the interested reader to a representative set of references @cite_30 @cite_32 @cite_10 @cite_39 @cite_31 @cite_46 @cite_35 . Other data structures, such as Interval Trees @cite_40 and Segment trees @cite_52 can also be used for storing temporal information. Temporal aggregation in scientific array databases @cite_50 is another related topic of interest, but the challenges there are significantly different.
@cite_30 @cite_35 @cite_31 @cite_46 @cite_32 @cite_52 @cite_39 @cite_40 @cite_50 @cite_47 @cite_10
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A temporal database contains time-varying data. In a real-time database transactions have deadlines or timing constraints. In this paper we review the substantial research in these two previously separate areas. First we characterize the time domain; then we investigate temporal and real-time data models. We evaluate temporal and real-time query languages along several dimensions. We examine temporal and real-time DBMS implementation. Finally, we summarize major research accomplishments to date and list several unanswered research questions. > In the coming big data era, stress test to IT systems under extreme data volume is crucial to the adoption of computing technologies in every corner of the cyber world. Appropriately generated benchmark datasets provide the possibility for administrators to evaluate the capacity of the systems when real datasets hard obtained have not extreme cases. Traditional benchmark data generators, however, mainly target at producing relation tables of arbitrary size following fixed distributions. The output of such generators are insufficient when it is used to measure the stability of the architecture with extremely dynamic and heavy workloads, caused by complicated hiden factors in the generation mechanism of real world, e.g. dependency between stocks in the trading market and collaborative human behaviors on the social network. In this paper, we present a new framework, called Chronos, to support new demands on streaming data benchmarking, by generating and simulating realistic and fast data streams in an elastic manner. Given a small group of samples with timestamps, Chronos reproduces new data streams with similar characteristics of the samples, preserving column-wise correlations, temporal dependency and order statistics of the snapshot distributions at the same time. To achieve such realistic requirements, we propose 1) a column decomposition optimization technique to partition the original relation table into small sub-tables with minimal correlation information loss, 2) a generative and extensible model based on Latent Dirichlet Allocation to capture temporal dependency while preserving order statistics of the snapshot distribution, and 3) a new generation and assembling method to efficiently build tuples following the expected distribution on the snapshots. To fulfill the vision of elasticity, we also present a new parallel stream data generation mechanism, facilitating distributed nodes to collaboratively generate tuples with minimal synchronization overhead and excellent load balancing. Our extensive experimental studies on real world data domains confirm the efficiency and effectiveness of Chronos on stream benchmark generation and simulation. Many relational domains contain temporal information and dynamics that are important to model (e.g., social networks, protein networks). However, past work in relational learning has focused primarily on modeling static "snapshots" of the data and has largely ignored the temporal dimension of these data. In this work, we extend relational techniques to temporally-evolving domains and outline a representational framework that is capable of modeling both temporal and relational dependencies in the data. We develop efficient learning and inference techniques within the framework by considering a restricted set of temporal-relational dependencies and using parameter-tying methods to generalize across relationships and entities. More specifically, we model dynamic relational data with a two-phase process, first summarizing the temporal-relational information with kernel smoothing, and then moderating attribute dependencies with the summarized relational information. We develop a number of novel temporal-relational models using the framework and then show that the current approaches to modeling static relational data are special cases within the framework. We compare the new models to the competing static relational methods on three real-world datasets and show that the temporal-relational models consistently outperform the relational models that ignore temporal information - achieving significant reductions in error ranging from 15 to 70 . From the Publisher: Temporal database systems are systems that provide special support for storing, querying, and updating historical and or future data. Current DBMSs provide essentially no temporal features at all, but this situation is likely to change soon for a variety of reasons; in fact, temporal databases are virtually certain to become important sooner rather than later, in the commercial world as well as in academia. This book provides an in-depth description of the foundations and principles on which those temporal DBMSs will be built. These foundations and principles are firmly rooted in the relational model of data; thus, they represent an evolutionary step, not a revolutionary one, and they will stand the test of time. This book is arranged in three parts and a set of appendixes: * Preliminaries: Provides a detailed review of the relational model, and an overview of the Tutorial D language. * Laying the Foundations: Explains basic temporal data problems and introduces fundamental constructs and operators for addressing those problems. * Building on the Foundations: Applies the material of the previous part to issues of temporal database design, temporal constraints, temporal query and update, and much more. * Appendixes: Include annotated references and bibliography, implementation considerations, and other topics. KEY FEATURES: * Describes a truly relational approach to the temporal data problem. * Addresses implementation as well as model issues. * Covers recent research on new database design techniques, a new normal form, new relational operators, new update operators, a new approach to the problem of granularity, support for cyclic pointtypes, and other matters. * Includes review questions and exercises in every chapter. * Suitable for both reference and tutorial purposes.