Indexes for large collections of intervals are common in temporal databases, where each record has a lifespan, or validity interval.We propose a universal representation that encapsulates various interval indexes using diagonal corner structures, providing valuable insights about their effectiveness.Moreover, we exploit our findings to develop TIDE, a disk-based index for historical intervals.TIDE adopts a two-level architecture.A top tree organizes intervals by their duration.The leaf nodes of the top tree correspond to the root nodes of bottom trees, ordering intervals by their endpoints.Both top and bottom trees are append-only B+-trees to facilitate fast insertions.An experimental evaluation with real data sets shows that TIDE achieves impressive performance gains with respect to its direct competitor, on insertion (up to x100) and query processing (up to x7000) speed.

Although several spatial indexes achieve fast query processing, they are ineffective for highly dynamic data sets because of costly updates. On the other hand, simple structures that enable efficient updates are slow for spatial queries. In this paper, we propose Waffle , a workload-aware, query-sensitive spatial index, that effectively accommodates both update- and query-intensive workloads. Waffle combines concepts of the space and data partitioning frameworks, and constitutes a complete indexing solution. In addition to query processing algorithms, it includes: (i) a novel bulk loading method that guarantees optimal disk page utilization on static data, (ii) algorithms for dynamic updates that guarantee zero overlapping of nodes, and (iii) a maintenance mechanism that adjusts the tradeoff between query and update speed, based on the workload and query distribution. An extensive experimental evaluation confirms the superiority of Waffle against state of the art space and data partitioning indexes on update and query efficiency.

In the last decade, civic crowdfunding has proved to be effective in generating funds for the provision of public projects. However, the existing literature deals only with citizen's with positive valuation and symmetric belief towards the project's provision. In this work, we present novel mechanisms which break these two barriers, i.e., mechanisms which incorporate negative valuation and asymmetric belief, independently. For negative valuation, we present a methodology for converting existing mechanisms to mechanisms that incorporate agents with negative valuations. Particularly, we adapt existing PPR and PPS mechanisms, to present novel PPRN and PPSN mechanisms which incentivize strategic agents to contribute to the project based on their true preference. With respect to asymmetric belief, we propose a reward scheme Belief Based Reward (BBR) based on Robust Bayesian Truth Serum mechanism. With BBR, we propose a general mechanism for civic crowdfunding which incorporates asymmetric agents. We leverage PPR and PPS, to present PPRx and PPSx. We prove that in PPRx and PPSx, agents with greater belief towards the project's provision contribute more than agents with lesser belief. Further, we also show that contributions are such that the project is provisioned at equilibrium.

Although peer prediction markets are widely used in crowdsourcing to aggregate information from agents, they often fail to reward the participating agents equitably. Honest agents can be wrongly penalized if randomly paired with dishonest ones. In this work, we introduce selective and cumulative fairness. We characterize a mechanism as fair if it satisfies both notions and present FaRM, a representative mechanism we designed. FaRM is a Nash incentive mechanism that focuses on information aggregation for spontaneous local activities which are accessible to a limited number of agents without assuming any prior knowledge of the event. All the agents in the vicinity observe the same information. FaRM uses (i) a report strength score to remove the risk of random pairing with dishonest reporters, (ii) a consistency score to measure an agent's history of accurate reports and distinguish valuable reports, (iii) a reliability score to estimate the probability of an agent to collude with nearby agents and prevents agents from getting swayed, and (iv) a location robustness score to filter agents who try to participate without being present in the considered setting. Together, report strength, consistency, and reliability represent a fair reward given to agents based on their reports.