We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

Aspect] is a simple and practical aspect-oriented extension to Java With just a few new constructs, AspectJ provides support for modular implementation of a range of crosscutting concerns. In AspectJ's dynamic join point model, join points are well-defined points in the execution of the program; pointcuts are collections of join points; advice are special method-like constructs that can be attached to pointcuts; and aspects are modular units of crosscutting implementation, comprising pointcuts, advice, and ordinary Java member declarations. AspectJ code is compiled into standard Java bytecode. Simple extensions to existing Java development environments make it possible to browse the crosscutting structure of aspects in the same kind of way as one browses the inheritance structure of classes. Several examples show that AspectJ is powerful, and that programs written using it are easy to understand.

An overview of AspectJ

Data warehousing and on-line analytical processing (OLAP) are essential elements of decision support, which has increasingly become a focus of the database industry. Many commercial products and services are now available, and all of the principal database management system vendors now have offerings in these areas. Decision support places some rather different requirements on database technology compared to traditional on-line transaction processing applications. This paper provides an overview of data warehousing and OLAP technologies, with an emphasis on their new requirements. We describe back end tools for extracting, cleaning and loading data into a data warehouse; multidimensional data models typical of OLAP; front end client tools for querying and data analysis; server extensions for efficient query processing; and tools for metadata management and for managing the warehouse. In addition to surveying the state of the art, this paper also identifies some promising research issues, some of which are related to problems that the database research community has worked on for years, but others are only just beginning to be addressed. This overview is based on a tutorial that the authors presented at the VLDB Conference, 1996.

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An overview of data warehousing and OLAP technology

Search operations in databases require special support at the physical level. This is true for conventional databases as well as spatial databases, where typical search operations include the point query (find all objects that contain a given search point) and the region query (find all objects that overlap a given search region). More than ten years of spatial database research have resulted in a great variety of multidimensional access methods to support such operations. We give an overview of that work. After a brief survey of spatial data management in general, we first present the class of point access methods, which are used to search sets of points in two or more dimensions. The second part of the paper is devoted to spatial access methods to handle extended objects, such as rectangles or polyhedra. We conclude with a discussion of theoretical and experimental results concerning the relative performance of various approaches.

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Multidimensional access methods

A large number of distributed applications requires continuous and timely processing of information as it flows from the periphery to the center of the system. Examples include intrusion detection systems which analyze network traffic in real-time to identify possible attacks; environmental monitoring applications which process raw data coming from sensor networks to identify critical situations; or applications performing online analysis of stock prices to identify trends and forecast future values.Traditional DBMSs, which need to store and index data before processing it, can hardly fulfill the requirements of timeliness coming from such domains. Accordingly, during the last decade, different research communities developed a number of tools, which we collectively call Information flow processing (IFP) systems, to support these scenarios. They differ in their system architecture, data model, rule model, and rule language. In this article, we survey these systems to help researchers, who often come from different backgrounds, in understanding how the various approaches they adopt may complement each other.In particular, we propose a general, unifying model to capture the different aspects of an IFP system and use it to provide a complete and precise classification of the systems and mechanisms proposed so far.

Processing flows of information: From data stream to complex event processing

The HiPAC (High Performance ACtive database system) project addresses two critical problems in time-constrained data management: the handling of timing constraints in databases, and the avoidance of wasteful polling through the use of situation-action rules that are an integral part of the database and are monitored by DBMS's condition monitor. A rich knowledge model provides the necessary primitives for definition of timing constraints, situation-action rules, and precipitating events. The execution model allows various coupling modes between transactions, situation evaluations and actions, and provides the framework for correct concurrent execution of transactions and triggered actions. Different approaches to scheduling of time-constrained tasks and transactions are explored and an architecture is being designed with special emphasis on the interaction of the time-constrained, active DBMS and the operating system. Performance models are developed to evaluate the various design alternatives.

The HiPAC project: combining active databases and timing constraints

Event-Condition-Action (ECA) Rules are proposed as a general mechanism for providing active database capabilities in support of applications that require timely response to critical situations. These rules generalize mechanisms such as assertions, triggers, alerters, database procedures, and production rules that have previously been proposed for supporting such DBMS functions as integrity control, access control, derived data management. and inferencing. This paper argues that ECA rules should be thought of as first class objects in an object-oriented data model. It identifies concepts for modelling the components and properties of rule objects: events (database operations, temporal events, abstract signals from arbitrary user processes, and complex events constructed from these primitive ones); conditions (queries over the database): actions (programs in the query language or some programming language); and coupling modes (which describe whether the event, condition, and action components of a rule should be executed in a single transaction or in separate transactions). The paper discusses the association of timing constraints and contingency plans with rules. Finally, it describes operations on rule objects. The emphasis of the paper is on modelling concepts, rather than on specific syntax.

Rules are objects too: A knowledge model for an active, object-oriented databasesystem

Complex Event Processing (CEP) has evolved into the paradigm of choice for the development of monitoring and reactive applications. It also has a strong impact on future information systems and the way we subscribe to and consume information. Besides being a highly active research field, CEP already plays an important role in many application areas like logistics, energy management, finance, or manufacturing processes. Its importance for information systems is expected to grow further with the increasing number of decentralized information sources, such as blogs, and with the deployment of tagging and sensing technology as well as its integration in real-world objects. CEP addresses two crucial prerequisites to build highly scalable and dynamic systems. First, it decouples providers and receivers of information. Neither the providers need knowledge about the set of relevant receivers, nor do receivers need to know the set of relevant data or event sources. Second, CEP-systems do not only mediate information in form of events between providers and consumers, but support the detection of relationships among events, for instance, temporal relations that can be specified by definition of correlation rules (often called Event Patterns). Through aggregation and composition new complex events can be generated and used subsequently to derive more abstract events. CEP provides a natural decoupling between basic events with a strong relationship to the semantics of the underlying technology (e. g. sensor readings) and complex events closer to the semantics of the application. Therefore, it enables information systems to perform independent reconfigurations at the technical and application level. Furthermore, the stepwise correlation of events can help to reduce the message load and thus contributes towards a highly scalable information system. Business applications are increasingly interconnected and can impose a massive event load to be processed by current CEP systems. Moreover, the importance of sensing devices in applications is expected to grow. In the future, the Internet of Things may comprise billions of sensing devices. CEP will be a tool to derive understandable information on the basis of a large number of events. In this context it is important to support the distribution of event correlation in the presence of highly dynamic systems and support mechanisms for self-organization. Guaranteeing non-functional properties, such as, reliability, availability, performance, and security pose major challenges on the technical infrastructure, while expressiveness of event languages, event derivation and usability are challenges to make CEP accessible to a broader user community. This special issue intends to provide an insight on the applications and principles as well as the evolution of CEP. Moreover, we believe the selected articles from International and German researchers illustrate current trends and challenges in designing powerful, scalable as well as secure event processing systems. The group of Sharma Chakravarthy has a long experience in the field of Complex Event Processing and has designed one of the early and very influential event correlation languages, called Snoop. The work by Chakravarthy et al. gives a historical view on the development as well as the roots of Complex Event Processing. Furthermore, the authors point out important applications and challenges that go hand-in-hand with the development of CEP Systems. Distributed event-based systems are the key to increase the scalability of today’s information systems. Jacobsen et al. present the PADRES system which addresses the design of adaptive event-based systems. In their approach they show how content-based publish/subscribe systems can be extended to account also for composition of complex events as well as enable uniform query mechanisms for events in the future and the past. While in the past many efforts have focused on the design of efficient and expressive event-based systems, it is often hard to estimate the performance of eventbased systems in combination with QoS requirements of applications. The work by Kounev and Sachs bridges this gap by reviewing and proposing performance models that fit Distributed Event-Based Systems particularly well. Although the benefits of distributed event-based systems are often highlighted by researchers, so far most industrial solutions still rely on centralized event processing technology. Pletat et al. identify in their article one obstacle in deploying distributed event processing

Complex Event Processing

Publish/subscribe systems are successfully used to decouple distributed applications. However, their efficiency is closely tied to the topology of the underlying network, the design of which has been neglected. Peer-to-peer network topologies can offer inherently bounded delivery depth, load sharing, and self-organisation. In this paper, we present a content-based publish/subscribe system routed over a peer-to-peer topology graph. The implications of combining these approaches are explored and a particular implementation using elements from Rebeca and Chord is proven correct.

A peer-to-peer approach to content-based publish/subscribe

Complex query types, huge data volumes, and very high read/update ratios make the indexing techniques designed and tuned for traditional database systems unsuitable for data warehouses (DW). We propose an encoded bitmap indexing for DWs which improves the performance of known bitmap indexing in the case of large cardinality domains. A performance analysis and theorems which identify properties of good encodings for better performance are presented. We compare encoded bitmap indexing with related techniques, such as bit slicing, projection-, dynamic-, and range-based indexing.

Alejandro Buchmann

Papers

The HiPAC project: combining active databases and timing constraints

Rules are objects too: A knowledge model for an active, object-oriented databasesystem

Complex Event Processing

A peer-to-peer approach to content-based publish/subscribe

Encoded bitmap indexing for data warehouses