Browsing by Author "Ferhatosmanoğlu, H."

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Open Access
ALACA: a platform for dynamic alarm collection and alert notification in network management systems
(John Wiley and Sons Ltd., 2017) Solmaz, S. E.; Gedik, B.; Ferhatosmanoğlu, H.; Sözüer, S.; Zeydan, E.; Etemoğlu, Ç. Ö.
Mobile network operators run Operations Support Systems that produce vast amounts of alarm events. These events can have different significance levels and domains and also can trigger other ones. Network operators face the challenge to identify the significance and root causes of these system problems in real time and to keep the number of remedial actions at an optimal level, so that customer satisfaction rates can be guaranteed at a reasonable cost. In this paper, we propose a scalable streaming alarm management system, referred to as Alarm Collector and Analyzer, that includes complex event processing and root cause analysis. We describe a rule mining and root cause analysis solution for alarm event correlation and analyses. The solution includes a dynamic index for matching active alarms, an algorithm for generating candidate alarm rules, a sliding window–based approach to save system resources, and a graph-based solution to identify root causes. Alarm Collector and Analyzer is used in the network operation center of a major mobile telecom provider. It helps operators to enhance the design of their alarm management systems by allowing continuous analysis of data and event streams and predict network behavior with respect to potential failures by using the results of root cause analysis. We present experimental results that provide insights on performance of real-time alarm data analytics systems. Copyright © 2017 John Wiley & Sons, Ltd.
Open Access
Cascade-aware partitioning of large graph databases
(Springer, 2019) Demirci, Gündüz Vehbi; Ferhatosmanoğlu, H.; Aykanat, Cevdet
Graph partitioning is an essential task for scalable data management and analysis. The current partitioning methods utilize the structure of the graph, and the query log if available. Some queries performed on the database may trigger further operations. For example, the query workload of a social network application may contain re-sharing operations in the form of cascades. It is beneficial to include the potential cascades in the graph partitioning objectives. In this paper, we introduce the problem of cascade-aware graph partitioning that aims to minimize the overall cost of communication among parts/servers during cascade processes. We develop a randomized solution that estimates the underlying cascades, and use it as an input for partitioning of large-scale graphs. Experiments on 17 real social networks demonstrate the effectiveness of the proposed solution in terms of the partitioning objectives.
Open Access
Distributed block formation and layout for disk-based management of large-scale graphs
(Springer, 2017) Yaşar, A.; Gedik, B.; Ferhatosmanoğlu, H.
We are witnessing an enormous growth in social networks as well as in the volume of data generated by them. An important portion of this data is in the form of graphs. In recent years, several graph processing and management systems emerged to handle large-scale graphs. The primary goal of these systems is to run graph algorithms and queries in an efficient and scalable manner. Unlike relational data, graphs are semi-structured in nature. Thus, storing and accessing graph data using secondary storage requires new solutions that can provide locality of access for graph processing workloads. In this work, we propose a scalable block formation and layout technique for graphs, which aims at reducing the I/O cost of disk-based graph processing algorithms. To achieve this, we designed a scalable MapReduce-style method called ICBL, which can divide the graph into a series of disk blocks that contain sub-graphs with high locality. Furthermore, ICBL can order the resulting blocks on disk to further reduce non-local accesses. We experimentally evaluated ICBL to showcase its scalability, layout quality, as well as the effectiveness of automatic parameter tuning for ICBL. We deployed the graph layouts generated by ICBL on the Neo4j open source graph database, http://www.neo4j.org/ (2015) graph database management system. Our results show that the layout generated by ICBL reduces the query running times over Neo4j more than 2 × compared to the default layout. © 2017, Springer Science+Business Media New York.
Open Access
Privacy-preserving aggregate queries for optimal location selection
(IEEE, 2019) Yılmaz, Emre; Ferhatosmanoğlu, H.; Ayday, Erman; Aksoy, Remzi Can
Today, vast amounts of location data are collected by various service providers. These location data owners have a good idea of where their users are most of the time. Other businesses also want to use this information for location analytics, such as finding the optimal location for a new branch. However, location data owners cannot share their data with other businesses, mainly due to privacy and legal concerns. In this paper, we propose privacy-preserving solutions in which location-based queries can be answered by data owners without sharing their data with other businesses and without accessing sensitive information such as the customer list of the businesses that send the query. We utilize a partially homomorphic cryptosystem as the building block of the proposed protocols. We prove the security of the protocols in semi-honest threat model. We also explain how to achieve differential privacy in the proposed protocols and discuss its impact on utility. We evaluate the performance of the protocols with real and synthetic datasets and show that the proposed solutions are highly practical. The proposed solutions will facilitate an effective sharing of sensitive data between entities and joint analytics in a wide range of applications without violating their customers' privacy.
Open Access
S3-TM: scalable streaming short text matching
(Association for Computing Machinery, 2015) Basık F.; Gedik, B.; Ferhatosmanoğlu, H.; Kalender, M. E.
Micro-blogging services have become major venues for information creation, as well as channels of information dissemination. Accordingly, monitoring them for relevant information is a critical capability. This is typically achieved by registering content-based subscriptions with the micro-blogging service. Such subscriptions are long-running queries that are evaluated against the stream of posts. Given the popularity and scale of micro-blogging services like Twitter and Weibo, building a scalable infrastructure to evaluate these subscriptions is a challenge. To address this challenge, we present the S3-TM system for streaming short text matching. S3-TM is organized as a stream processing application, in the form of a data parallel flow graph designed to be run on a data center environment. It takes advantage of the structure of the publications (posts) and subscriptions to perform the matching in a scalable manner, without broadcasting publications or subscriptions to all of the matcher instances. The basic design of S$$^3$$3-TM uses a scoped multicast for publications and scoped anycast for subscriptions. To further improve throughput, we introduce publication routing algorithms that aim at minimizing the scope of the multicasts. First set of algorithms we develop are based on partitioning the word co-occurrence frequency graph, with the aim of routing posts that include commonly co-occurring words to a small set of matchers. While effective, these algorithms fell short in balancing the load. To address this, we develop the SALB algorithm, which provides better load balance by modeling the load more accurately using the word-to-post bipartite graph. We also develop a subscription placement algorithm, called LASP, to group together similar subscriptions, in order to minimize the subscription matching cost. Furthermore, to achieve good scalability for increasing number of nodes, we introduce techniques to handle workload skew. Finally, we introduce load shedding techniques for handling unexpected load spikes with small impact on the accuracy. Our experimental results show that S3-TM is scalable. Furthermore, the SALB algorithm provides more than 2.5× throughput compared to the baseline multicast and outperforms the graph partitioning-based approaches.
Open Access
SLIM: scalable linkage of mobility data
(Association for Computing Machinery, 2020) Basık, F.; Ferhatosmanoğlu, H.; Gedik, Buğra
We present a scalable solution to link entities across mobility datasets using their spatio-temporal information. This is a fundamental problem in many applications such as linking user identities for security, understanding privacy limitations of location based services, or producing a unified dataset from multiple sources for urban planning. Such integrated datasets are also essential for service providers to optimise their services and improve business intelligence. In this paper, we first propose a mobility based representation and similarity computation for entities. An efficient matching process is then developed to identify the final linked pairs, with an automated mechanism to decide when to stop the linkage. We scale the process with a locality-sensitive hashing (LSH) based approach that significantly reduces candidate pairs for matching. To realize the effectiveness and efficiency of our techniques in practice, we introduce an algorithm called SLIM. In the experimental evaluation, SLIM outperforms the two existing state-of-the-art approaches in terms of precision and recall. Moreover, the LSH-based approach brings two to four orders of magnitude speedup.
Open Access
Spatio-temporal linkage over location-enhanced services
(Institute of Electrical and Electronics Engineers, 2018) Basık, F.; Gedik, B.; Etemoğlu, Ç.; Ferhatosmanoğlu, H.
We are witnessing an enormous growth in the volume of data generated by various online services. An important portion of this data contains geographic references, since many of these services are location-enhanced and thus produce spatio-temporal records of their usage. We postulate that the spatio-temporal usage records belonging to the same real-world entity can be matched across records from different location-enhanced services. Linking spatio-temporal records enables data analysts and service providers to obtain information that they cannot derive by analyzing only one set of usage records. In this paper, we develop a new linkage model that can be used to match entities from two sets of spatio-temporal usage records belonging to two different location-enhanced services. This linkage model is based on the concept of $k$- $l$ diversity —that we developed to capture both spatial and temporal aspects of the linkage. To realize this linkage model in practice, we develop a scalable linking algorithm called ST-Link, which makes use of effective spatial and temporal filtering mechanisms that significantly reduce the search space for matching users. Furthermore, ST-Link utilizes sequential scan procedures to avoid random disk access and thus scales to large datasets. We evaluated our work with respect to accuracy and performance using several datasets. Experiments show that ST-Link is effective in practice for performing spatio-temporal linkage and can scale to large datasets.