Emerging providers of online services offer access to data collections. Such data service providers need to build data structures, e.g. materialized views and indexes, in order to offer better performance for user query execution. The cost of such structures is charged to the user as part of the overall query service cost. In order to ensure the economic viability of the provider, the building and maintenance cost of new structures has to be amortized to a set of prospective query services that will use them. This work proposes a novel stochastic model that predicts the extent of cost amortization in time and number of services. The model is completed with a novel method that regresses query traffic statistics and provides input to the prediction model. In order to demonstrate the effectiveness of the prediction model, we study its application on an extension of an existing economy model for the management of a cloud DBMS. A thorough experimental study shows that the prediction model ensures the economic viability of the cloud DBMS while enabling the offer of fast and cheap query services.

Index tuning, i.e., selecting the indexes appropriate for a workload, is a crucial problem in database system tuning. In this paper, we solve index tuning for large problem instances that are common in practice, e.g., thousands of queries in the workload, thousands of candidate indexes and several hard and soft constraints. Our work is the first to reveal that the index tuning problem has a well structured space of solutions, and this space can be explored efficiently with well known techniques from linear optimization. Experimental results demonstrate that our approach outperforms state-of-the-art commercial and research techniques by a significant margin (up to an order of magnitude).

Cloud applications that offer data management services are emerging. Such clouds support caching of data in order to provide quality query services. The users can query the cloud data, paying the price for the infrastructure they use. Cloud management necessitates an economy that manages the service of multiple users in an efficient, but also, resource-economic way that allows for cloud profit. Naturally, the maximization of cloud profit given some guarantees for user satisfaction presumes an appropriate price-demand model that enables optimal pricing of query services. The model should be plausible in that it reflects the correlation of cache structures involved in the queries. Optimal pricing is achieved based on a dynamic pricing scheme that adapts to time changes. This paper proposes a novel price-demand model designed for a cloud cache and a dynamic pricing scheme for queries executed in the cloud cache. The pricing solution employs a novel method that estimates the correlations of the cache services in an time-efficient manner. The experimental study shows the efficiency of the solution.

One of the most challenging tasks for the database administrator is to physically design the database to attain optimal performance for a given workload. Physical design is hard because it requires the selection of an optimal set of design features from a vast search space. There have been many commercial tools available to automatically suggest the physical design, for a given a set of queries. These tools are, however, based on greedy heuristic pruning, which reduces their usefulness. Furthermore, they are not interactive, as the APIs to simulate the indexes and tables are product specific and hidden from the database administrators. Finally, all these tools are built specifically for commercial systems and there is lack of automated physical designers for open source DBMSs. In this demonstration we introduce -PARINDA - an interactive physical designer for an open source DBMS. Given a workload containing a set of queries, this tool allows the DBA to efficiently simulate various physical design features and get immediate feedback on their effectiveness. It also incorporates recent advances in non-greedy physical design techniques to provide close to optimal suggestions. Although it has been prototyped for several different DBMSs, we demonstrate the usefulness and efficiency of the tool while running on the open source DBMS---PostgreSQL--using large real-world scientific datasets and query workloads.

Tuning tools attempt to configure a database to achieve optimal performance for a given workload. Selecting an optimal set of physical structures is computationally hard since it involves searching a vast space of possible configurations. Commercial DBMSs offer tools that can address this problem. The usefulness of such tools, however, is limited by their dependence on greedy heuristics, the need for a-priori (offline) knowledge of the workload, and lack of an optimal materialization schedule to get the best out of suggested design features. Moreover, the open source DBMSs do not provide any automated tuning tools.

Cloud computing, the new trend for service infrastructures requires user multi-tenancy as well as minimal capital expenditure. In a cloud that services large amounts of data that are massively collected and queried, such as scientific data, users typically pay for query services. The cloud supports caching of data in order to provide quality query services. User payments cover query execution costs and maintenance of cloud infrastructure, and incur cloud profit. The challenge resides in providing efficient and resource-economic query services while maintaining a profitable cloud. In this work we propose an economic model for self-tuned cloud caching targeting the service of scientific data. The proposed economy is adapted to policies that encourage high-quality individual and overall query services but also brace the profit of the cloud. We propose a cost model that takes into account all possible query and infrastructure expenditure. The experimental study proves that the proposed solution is viable for a variety of workloads and data.

We propose a dynamic faceted search system for discovery-driven analysis on data with both textual content and structured attributes. From a keyword query, we want to dynamically select a small set of "interesting" attributes and present aggregates on them to a user. Similar to work in OLAP exploration, we define "interestingness" as how surprising an aggregated value is, based on a given expectation. We make two new contributions by proposing a novel "navigational" expectation that's particularly useful in the context of faceted search, and a novel interestingness measure through judicious application of p-values. Through a user survey, we find the new expectation and interestingness metric quite effective. We develop an efficient dynamic faceted search system by improving a popular open source engine, Solr. Our system exploits compressed bitmaps for caching the posting lists in an inverted index, and a novel directory structure called a bitset tree for fast bitset intersection. We conduct a comprehensive experimental study on large real data sets and show that our engine performs 2 to 3 times faster than Solr.

Despite the existence of several secure BGP routing protocols, there has been little progress to date on actual adoption. Although feasibility for widespread adoption remains the greatest hurdle for BGP security, there has been little quantitative research into what properties contribute the most to the adoptability of a security scheme. In this paper, we provide a model for assessing the adoptability of a secure BGP routing protocol. We perform this evaluation by simulating incentives compatible adoption decisions of ISPs on the Internet under a variety of assumptions. Our results include: (a) the existence of a sharp threshold, where, if the cost of adoption is below the threshold, complete adoption takes place, while almost no adoption takes place above the threshold; (b) under a strong attacker model, adding a single hop of path authentication to origin authentication yields similar adoptability characteristics as a full path security scheme; (c) under a weaker attacker model, adding full path authentication (e.g., via S-BGP [9]) significantly improves the adoptability of BGP security over weaker path security schemes such as soBGP [16]. These results provide insight into the development of more adoptable secure BGP protocols and demonstrate the importance of studying adoptability of protocols.