The proliferation of GPS-enabled devices has led to the development of numerous location-based services. These services need to process massive amounts of streamed spatial data in real-time. The current scale of spatial data cannot be handled using centralized systems. This has led to the development of distributed spatial streaming systems. Existing systems are using static spatial partitioning to distribute the workload. In contrast, the real-time streamed spatial data follows non-uniform spatial distributions that are continuously changing over time. Distributed spatial streaming systems need to react to the changes in the distribution of spatial data and queries. This article introduces SWARM, a lightweight adaptivity protocol that continuously monitors the data and query workloads across the distributed processes of the spatial data streaming system and redistributes and rebalances the workloads as soon as performance bottlenecks get detected. SWARM is able to handle multiple query-execution and data-persistence models. A distributed streaming system can directly use SWARM to adaptively rebalance the system’s workload among its machines with minimal changes to the original code of the underlying spatial application. Extensive experimental evaluation using real and synthetic datasets illustrate that, on average, SWARM achieves 2 improvement in throughput over a static grid partitioning that is determined based on observing a limited history of the data and query workloads. Moreover, SWARM reduces execution latency on average 4 compared with the other technique.

Preserving users’ privacy while exploring the Internet is a continuous challenge. Regulations such as GDPR, CCPA, and PDPL have been introduced to counter privacy-invasive trends. This paper introduces a framework for inspecting the state of popular websites in preserving the privacy of their visitors. The framework can be used to analyze websites in both Arabic and English languages. We use it to report the current state of popular websites in Saudi Arabia. We examined the state of these websites three months before the official enforcement of the Personal Data Protection Law (PDPL). Therefore, the paper shows the degree of alignment for popular websites in Saudi Arabia with PDPL. We analyze 723 websites and explore their utilization of cookies. We examined whether these websites inform their visitors of cookie usage and obtain visitors’ consent. We assess whether these websites detail their privacy policies to their visitors. Our results demonstrated that approximately 85% of websites that use cookies don’t display cookie banners or policies to their visitors, potentially depriving users from their PDPL-specified rights such as being informed of data collection. Our analysis shows that 39% of the scanned websites do not follow the PDPL requirement of providing privacy policies, precluding users from knowing how their collected data will be handled and how they can exercise their rights under PDPL. In summary, our analysis highlights alarming practices that can potentially violate PDPL requirements.

The performance of distributed streaming systems relies on how even the workload is distributed among their machines. However, data and query workloads are skewed and change rapidly. Therefore, multiple adaptive load-balancing mechanisms have been proposed in the literature to rebalance distributed streaming systems according to the changes in their workloads. This paper introduces a novel attack model that targets adaptive load-balancing mechanisms of distributed streaming systems. The attack reduces the throughput and the availability of the system by making it stay in a continuous state of rebalancing. This paper proposes Guard, a component that detects and blocks attacks that target the adaptive load balancing of distributed streaming systems. Guard uses an unsupervised machine-learning technique to detect malicious users that are involved in the attack. Guard does not block any user unless it detects that the user is malicious. Guard does not depend on a specific application. Experimental evaluation for a high-intensity attack illustrates that Guard improves the throughput and the availability of the system by 85% and 86%, respectively. Moreover, Guard improves the minimum availability that the attacker achieves by 325%.

Due to the ubiquity of mobile phones and location-detection devices, location data is being generated in very large volumes. Queries and operations that are performed on location data warrant the use of database systems. Despite that, location data is being supported in data systems as an afterthought. Typically, relational or NoSQL data systems that are mostly designed with non-location data in mind get extended with spatial or spatiotemporal indexes, some query operators, and higher level syntactic sugar in order to support location data. The ubiquity of location data and location data services call for systems that are solely designed and optimized for the efficient support of location data. This paper envisions designing intelligent location+X data systems, ILX for short, where location is treated as a first-class citizen type. ILX is tailored with location data as the main data type (location-first). Because location data is typically augmented with other data types X, e.g., graphs, text data, click streams, annotations, etc., ILX needs to be extensible to support other data types X along with location. This paper envisions the main features that ILX should support, and highlights research challenges in realizing and supporting ILX.