Aggregate Continuous Queries (ACQs) are both a very popular class of Continuous Queries (CQs) and also have a potentially high execution cost. As such, optimizing the processing of ACQs is imperative for Data Stream Management Systems (DSMSs) to reach their full potential in supporting (critical) monitoring applications. For multiple ACQs that vary in window specifications and pre-aggregation filters, existing multiple ACQs optimization schemes assume a processing model where each ACQ is computed as a final-aggregation of a sub-aggregation. In this paper, we propose a novel processing model for ACQs, called Tri Ops, with the goal of minimizing the repetition of operator execution at the sub-aggregation level. We also propose Tri Weave, a Tri Ops-aware multi-query optimizer. We analytically and experimentally demonstrate the performance gains of our proposed schemes which shows their superiority over alternative schemes. Finally, we generalize Tri Weave to incorporate the classical subsumption-based multi-query optimization techniques.

Data Streams Management Systems are designed to support monitoring applications, which require the processing of hundreds of Aggregate Continuous Queries (ACQs). These ACQs typically have different time granularities, with possibly different selection predicates and group-by attributes. In order to achieve scalability in the presence of heavy workloads, in this paper, we introduce the concept of 'Weaveability' as an indicator of the potential gains of sharing the processing of ACQs. We then propose Weave Share, a cost-based optimizer that exploits weaveability to optimize the shared processing of ACQs. Our experimental analysis shows that Weave Share outperforms the alternative sharing schemes generating up to four orders of magnitude better quality plans. Finally, we describe a practical implementation of the Weave Share optimizer.

Data privacy laws have appeared recently, such as the HIPAA laws for protecting medical records, and the PCI guidelines for protecting Credit Card information. Data privacy can be defined as maintaining the privacy of Personal Identifiable Information (PII) from unauthorized accessing. PII includes any piece of data that can be used alone, or in conjunction with additional information, to uniquely identify an individual. Examples of such information include national identification numbers, credit card numbers, as well as financial and medical records. Access control methods and data encryption provide a level of data protection from unauthorized access, however, it is not enough; it does not prohibit identity thefts. It was reported that 70% of the data privacy breaches are internal breaches that involve an employee from the enterprise who has access to some training or testing database replica, which contains all the PII. In addition to access control, we need techniques to obfuscate (i.e., mask or dim) the datasets used for training, testing and analysis purposes. A good data obfuscation technique would, among other features, preserve the data usability while protecting its privacy. This challenge is further complicated when real time requirements are added. In this paper we present BronzeGate: Obfuscated GoldenGate, the GoldenGate's real-time solution for transactional data privacy while maintaining data usability. BronzeGate utilizes different obfuscation functions for different data types to securely obfuscate the data, on real-time, while maintaining its statistical characteristics.

Amazon, Google, and IBM now sell cloud computing services.We consider the setting of a for-profit business selling data stream monitoring/management services and we investigate auction-based mechanisms for admission control of continuous queries. When submitting a query, each user also submits a bid of how much she is willing to pay for that query to run. The admission control auction mechanism then determines which queries to admit, and how much to charge each user in a way that maximizes system revenue while being strategyproof and sybil immune, incentivizing users to use the system honestly. Specifically, we require that each user maximizes her payoff by bidding her true value of having her query run. We design several payment mechanisms and experimentally evaluate them. We describe the provable game theoretic characteristics of each mechanism alongside its performance with respect to maximizing profit and total user payoff.

In highly interactive dynamic Web database systems, user satisfaction determines their success. In such systems, user requested web pages are dynamically created by executing a number of database queries or Web transactions. In this paper, we model the interrelated transactions generating a web page asworkflowsand quantify the user satisfaction by associating dynamic Web pages withsoft-deadlines. Further, we model the importance of transactions in generating a page by associating different weights to transactions. Using this framework, system success is measured in terms of minimizing the deviation from the deadline (i.e., tardiness) and also minimizing the weighted such deviation (i.e., weighted tardiness). In order to efficiently support the materialization of dynamic Web pages, we proposeASETS*, which is a parameter-free adaptive scheduling algorithm that automatically adapts to, not only system load, but also transactions' characteristics (i.e., interdependencies, deadlines and weights).ASETS* prioritizes the execution of transactions with the objective of minimizing weighted tardiness. It is also capable of balancing the tradeoff between optimizing average- and worst-case performance when needed. The performance advantages ofASETS* are experimentally demonstrated.