The advent of affordable, shared-nothing computing systems portends a new class of parallel database management systems (DBMS) for on-line transaction processing (OLTP) applications that scale without sacrificing ACID guarantees [7,9]. The performance of these DBMSs is predicated on the existence of an optimal database design that is tailored for the unique characteristics of OLTP workloads [43]. Deriving such designs for modern DBMSs is difficult, especially for enterprise-class OLTP systems, since they impose extra challenges: the use of stored procedures, the need for load balancing in the presence of time-varying skew, complex schemas, and deployments with larger number of partitions.To this purpose, we present a novel approach to automatically partitioning databases for enterprise-class OLTP systems that significantly extends the state of the art by: (1) minimizing the number distributed transactions, while concurrently mitigating the effects of temporal skew in both the data distribution and accesses, (2) extending the design space to include replicated secondary indexes, (4) organically handling stored procedure routing, and (3) scaling of schema complexity, data size, and number of partitions. This effort builds on two key technical contributions: an analytical cost model that can be used to quickly estimate the relative coordination cost and skew for a given workload and a candidate database design, and an informed exploration of the huge solution space based on large neighborhood search. To evaluate our methods, we integrated our database design tool with a high-performance parallel, main memory DBMS and compared our methods against both popular heuristics and a state-of-the-art research prototype [17]. Using a diverse set of benchmarks, we show that our approach improves throughput by up to a factor of 16× over these other approaches.

Context-aware systems are pervading everyday life, therefore context modeling is becoming a relevant issue and an expanding research field. This survey has the goal to provide a comprehensive evaluation framework, allowing application designers to compare context models with respect to a given target application; in particular we stress the analysis of those features which are relevant for the problem of data tailoring. The contribution of this paper is twofold: a general analysis framework for context models and an upto-date comparison of the most interesting, data-oriented approaches available in the literature.

Supporting graceful schema evolution represents an unsolved problem for traditional information systems that is further exacerbated in web information systems, such as Wikipedia and public scientific databases: in these projects based on multiparty cooperation the frequency of database schema changes has increased while tolerance for downtimes has nearly disappeared. As of today, schema evolution remains an error-prone and time-consuming undertaking, because the DB Administrator (DBA) lacks the methods and tools needed to manage and automate this endeavor by (i) predicting and evaluating the effects of the proposed schema changes, (ii) rewriting queries and applications to operate on the new schema, and (iii) migrating the database. Our PRISM system takes a big first step toward addressing this pressing need by providing: (i) a language of Schema Modification Operators to express concisely complex schema changes, (ii) tools that allow the DBA to evaluate the effects of such changes, (iii) optimized translation of old queries to work on the new schema version, (iv) automatic data migration, and (v) full documentation of intervened changes as needed to support data provenance, database flash back, and historical queries. PRISM solves these problems by integrating recent theoretical advances on mapping composition and invertibility, into a design that also achieves usability and scalability. Wikipedia and its 170+ schema versions provided an invaluable testbed for validating PRISM tools and their ability to support legacy queries.

In most enterprises, databases are deployed on dedicated database servers. Often, these servers are underutilized much of the time. For example, in traces from almost 200 production servers from different organizations, we see an average CPU utilization of less than 4%. This unused capacity can be potentially harnessed to consolidate multiple databases on fewer machines, reducing hardware and operational costs. Virtual machine (VM) technology is one popular way to approach this problem. However, as we demonstrate in this paper, VMs fail to adequately support database consolidation, because databases place a unique and challenging set of demands on hardware resources, which are not well-suited to the assumptions made by VM-based consolidation.Instead, our system for database consolidation, named Kairos, uses novel techniques to measure the hardware requirements of database workloads, as well as models to predict the combined resource utilization of those workloads. We formalize the consolidation problem as a non-linear optimization program, aiming to minimize the number of servers and balance load, while achieving near-zero performance degradation. We compare Kairos against virtual machines, showing up to a factor of 12× higher throughput on a TPC-C-like benchmark. We also tested the effectiveness of our approach on real-world data collected from production servers at Wikia.com, Wikipedia, Second Life, and MIT CSAIL, showing absolute consolidation ratios ranging between 5.5:1 and 17:1.

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Common to all actors in today's information world is the problem of lowering the "information noise," both reducing the amount of data to be stored and accessed, and enhancing the "precision" according to which the available data fit the application requirements