Column-stores gained popularity as a promising physical design alternative. Each attribute of a relation is physically stored as a separate column allowing queries to load only the required attributes. The overhead incurred is on-the-fly tuple reconstruction for multi-attribute queries. Each tuple reconstruction is a join of two columns based on tuple IDs, making it a significant cost component. The ultimate physical design is to have multiple presorted copies of each base table such that tuples are already appropriately organized in multiple different orders across the various columns. This requires the ability to predict the workload, idle time to prepare, and infrequent updates.In this paper, we propose a novel design, partial sideways cracking, that minimizes the tuple reconstruction cost in a self-organizing way. It achieves performance similar to using presorted data, but without requiring the heavy initial presorting step itself. Instead, it handles dynamic, unpredictable workloads with no idle time and frequent updates. Auxiliary dynamic data structures, called cracker maps, provide a direct mapping between pairs of attributes used together in queries for tuple reconstruction. A map is continuously physically reorganized as an integral part of query evaluation, providing faster and reduced data access for future queries. To enable flexible and self-organizing behavior in storage-limited environments, maps are materialized only partially as demanded by the workload. Each map is a collection of separate chunks that are individually reorganized, dropped or recreated as needed. We implemented partial sideways cracking in an open-source column-store. A detailed experimental analysis demonstrates that it brings significant performance benefits for multi-attribute queries.

Data exploration is about efficiently extracting knowledge from data even if we do not know exactly what we are looking for. In this tutorial, we survey recent developments in the emerging area of database systems tailored for data exploration. We discuss new ideas on how to store and access data as well as new ideas on how to interact with a data system to enable users and applications to quickly figure out which data parts are of interest. In addition, we discuss how to exploit lessons-learned from past research, the new challenges data exploration crafts, emerging applications and future research directions.

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A cracked database is a datastore continuously reorganized based on operations being executed. For each query, the data of interest is physically reclustered to speed-up future access to the same, overlapping or even disjoint data. This way, a cracking DBMS self-organizes and adapts itself to the workload.So far, cracking has been considered for static databases only. In this paper, we introduce several novel algorithms for high-volume insertions, deletions and updates against a cracked database. We show that the nice performance properties of a cracked database can be maintained in a dynamic environment where updates interleave with queries. Our algorithms comply with the cracking philosophy, i.e., a table is informed on pending insertions and deletions, but only when the relevant data is needed for query processing just enough pending update actions are applied.We discuss details of our implementation in the context of an open-source DBMS and we show through a detailed experimental evaluation that our algorithms always manage to keep the cost of querying a cracked datastore with pending updates lower than the non-cracked case.

We study the problem of evaluating conjunctive queries composed of triple patterns over RDF data stored in distributed hash tables. Our goal is to develop algorithms that scale to large amounts of RDF data, distribute the query processing load evenly and incur little network traffic. We present and evaluate two novel query processing algorithms with these possibly conflicting goals in mind. We discuss the various tradeoffs that occur in our setting through a detailed experimental evaluation of the proposed algorithms. This work was supported in part by the European Commission project Ontogrid (http://www.ontogrid.net/).