Ismail Toroslu - Academia.edu (original) (raw)

Papers by Ismail Toroslu

IEEE Access

Event detection is a popular research problem aiming to detect events from various data sources, ... more Event detection is a popular research problem aiming to detect events from various data sources, such as news texts, social media postings or social interaction patterns. In this work, event detection is studied on social interaction and communication data via tracking changes in community structure and communication trends. With this aim, various community structure and communication trend based event detection methods are proposed. Additionally, a new strategy called community size range based change tracking is presented such that the proposed algorithms can focus on communities with different size ranges, and considerable time efficiency can be obtained. The event detection performance of the proposed methods is analyzed using a set of real world and benchmark data sets in comparison to previous solutions in the literature. The experiments show that the proposed methods have higher event detection accuracy than the baseline methods. Additionally, their scalability is presented through analysis by using high volume of communication data. Among the proposed methods, CN-NEW, which is a community structure based method, performs the best on the overall. The proposed communication trend based methods perform better mostly on communication data sets (such as CDR), whereas community structure based methods tend to perform better on social media-based data sets.

Arxiv preprint arXiv:0804.1409, Apr 9, 2008

Abstract: Web usage mining is a type of web mining, which exploits data mining techniques to disc... more Abstract: Web usage mining is a type of web mining, which exploits data mining techniques to discover valuable information from navigation behavior of World Wide Web users. As in classical data mining, data preparation and pattern discovery are the main issues in web usage mining. The first phase of web usage mining is the data processing phase, which includes the session reconstruction operation from server logs. Session reconstruction success directly affects the quality of the frequent patterns discovered in the next phase. In ...

First International Workshop on Similarity Search and Applications (sisap 2008), 2008

Similarity search in sequence databases is of paramount importance in bioinformatics research. As... more Similarity search in sequence databases is of paramount importance in bioinformatics research. As the size of the genomic databases increases, similarity search of proteins in these databases becomes a bottleneck in large-scale studies, calling for more efficient methods of content-based retrieval. In this study, we present a metric-preserving, landmark-guided embedding approach to represent sequences in the vector domain in order to allow efficient indexing and similarity search. We analyze various properties of the embedding and show that the approximation achieved by the embedded representation is sufficient to achieve biologically relevant results. The approximate representation is shown to provide several orders of magnitude speed-up in similarity search compared to the exact representation, while maintaining comparable search accuracy.

Journal of Database Management, 1998

IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews), 2007

Abstract Producing answers to a set of queries with common tasks efficiently is known as the mult... more Abstract Producing answers to a set of queries with common tasks efficiently is known as the multiple-query optimization (MQO) problem. Each query can have several alternative evaluation plans, each with a different set of tasks. Therefore, the goal of MQO is to choose the right set of plans for queries which minimizes the total execution time by performing common tasks only once. Since MQO is an NP-hard problem, several, mostly heuristics based, solutions have been proposed for solving it. To the best of our knowledge, this ...

Data & Knowledge Engineering, 2017

Abstract Inference problem has always been an important and challenging topic of data privacy in ... more Abstract Inference problem has always been an important and challenging topic of data privacy in databases. In relational databases, the traditional solution to this problem was to define views on relational schemas to restrict the subset of attributes and operations available to the users in order to prevent unwanted inferences. This method is a form of decomposition strategy, which mainly concentrates on the granularity of the accessible fields to the users, to prevent sensitive information inference. Nowadays, due to increasing data sharing among parties, the possibility of constructing complex indirect methods to obtain sensitive data has also increased. Therefore, we need to not only consider security threats due to direct access to sensitive data but also address indirect inference channels using functional and probabilistic dependencies (e.g., deducing gender of an individual from his/her name) while creating security views. In this paper, we propose a proactive and decomposition based inference control strategy for relational databases to prevent direct or indirect inference of private data. We introduce a new kind of context dependent attribute policy rule, which is named as security dependent set, as a set of attributes whose association should not be inferred. Then, we define a logical schema decomposition algorithm that prevents inference among attributes in security dependent set. The decomposition algorithm takes both functional and probabilistic dependencies into consideration in order to prevent all kinds of known inferences of relations among the attributes of security dependent sets. We prove that our proposed decomposition algorithm generates a secure logical schema that complies with the given security dependent set constraints. Since our proposed technique is purely proactive, it does not require any prior knowledge about executed queries and do not need to modify any submitted queries. It can also be embedded into any relational database management system without changing anything in the underlying system. We empirically compare our proposed method with the state of art reactive methods. Our extensive experimental analysis, conducted using TPC-H 1 benchmark scheme, shows the effectives our proposed approach.

Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-Based Social Networks - LBSN '11, 2011

Social networks have evolved with the combination of geographical data, into Geo-social networks ... more Social networks have evolved with the combination of geographical data, into Geo-social networks (GSNs). GSNs give users the opportunity, not only to communicate with each other, but also to share images, videos, locations, and activities. The latest developments in GSNs incorporate the usage of location tracking services, such as GPS to allow users to "check in" at various locations and record their experience. In particular, users submit ratings or personal comments for their location/activity. The vast amount of data that is being generated by users with GPS devices, such as mobile phones, needs efficient methods for its effective management. In this paper, we have implemented an online prototype system, called Geo-social recommender system, where users can get recommendations on friends, locations and activities. For the friend recommendation task, we apply the FriendLink algorithm, which performs a local path traversal on the friendship network. In order to provide location/activity recommendations, we represent data by a 3-order tensor, on which latent semantic analysis and dimensionality reduction is performed using the Higher Order Singular Value Decomposition (HOSVD) technique. As more data is accumulated to the system, we use incremental solutions to update our tensor. We perform an experimental evaluation of our method with two real data sets and measure its effectiveness through recall/precision.

Information Processing Letters, 2004

The multiple query optimization (MQO) prob-lem has been studied in the database literature sinc... more The multiple query optimization (MQO) prob-lem has been studied in the database literature since 1980s. MQO tries to reduce the execution cost of a group of queries by performing common tasks only once, whereas traditional query optimization consid-ers a single query at a ...

Web usage mining is a type of web mining, which exploits data mining techniques to discover valua... more Web usage mining is a type of web mining, which exploits data mining techniques to discover valuable information from navigation behavior of World Wide Web users. As in classical data mining, data preparation and pattern discovery are the main issues in web usage mining. The first phase of web usage mining is the data processing phase, which includes the session reconstruction operation from server logs. Session reconstruction success directly affects the quality of the frequent patterns discovered in the next phase. In reactive web usage mining techniques, the source data is web server logs and the topology of the web pages served by the web server domain. Other kinds of information collected during the interactive browsing of web site by user, such as cookies or web logs containing similar information, are not used. The next phase of web usage mining is discovering frequent user navigation patterns. In this phase, pattern discovery methods are applied on the reconstructed sessions...

Traditional database access control mechanisms use role based methods, with generally row based a... more Traditional database access control mechanisms use role based methods, with generally row based and attribute based constraints for granularity, and privacy is achieved mainly by using views. However if only a set of views according to policy are made accessible to users, then this set should be checked against the policy for the whole probable query history. The aim of this work is to define a proactive decomposition algorithm according to the attribute based policy rules and build a secure logical schema in which relations are decomposed into several ones in order to inhibit joins or inferences that may violate predefined privacy constraints. The attributes whose association should not be inferred, are defined as having security dependency among them and they form a new kind of context dependent attribute based policy rule named as security dependent set. The decomposition algorithm works on a logical schema with given security dependent sets and aims to prohibit the inference of ...

Relational DBMSs continue to dominate the database market, and inference problem on external sche... more Relational DBMSs continue to dominate the database market, and inference problem on external schema of relational DBMS's is still an important issue in terms of data privacy.Especially for the last 10 years, external schema construction for application-specific database usage has increased its independency from the conceptual schema, as the definitions and implementations of views and procedures have been optimized. This paper offers an optimized decomposition strategy for the external schema, which concentrates on the privacy policy and required associations of attributes for the intended user roles. The method proposed in this article performs a proactive decomposition of the external schema, in order to satisfy both the forbidden and required associations of attributes.Functional dependency constraints of a database schema can be represented as a graph, in which vertices are attribute sets and edges are functional dependencies. In this representation, inference problem can be...

Multi-relational data mining has become popular due to the limitations of propositional problem d... more Multi-relational data mining has become popular due to the limitations of propositional problem definition in structured domains and the tendency of storing data in relational databases. Several relational knowledge discovery systems have been developed employing various search strategies, heuris-tics, language pattern limitations and hypothesis evaluation criteria, in order to cope with intractably large search space and to be able to generate high-quality patterns. In this work, a new ILP-based concept discovery method is described in which user-defined specifications are relaxed. Moreover, this new method directly works on relational databases. In addition to this, a new confidence-based pruning is used in this technique. A set of experiments are conducted to test the performance of the new method.

Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval - SIGIR '15, 2015

Our goal in this paper is to design cost-aware result caching approaches for meta-search engines.... more Our goal in this paper is to design cost-aware result caching approaches for meta-search engines. We introduce different levels of eviction, namely, query-, resource-and entry-level, based on the granularity of the entries to be evicted from the cache when it is full. We also propose a novel entrylevel caching approach that is tailored for the meta-search scenario and superior to alternative approaches.

Blogosphere plays an increasingly important role as a forum for public debate. In this paper, giv... more Blogosphere plays an increasingly important role as a forum for public debate. In this paper, given a mixed set of blogs debating a set of political issues from opposing camps, we use signed bipartite graphs for modeling debates, and we propose an algorithm for partitioning both the blogs, and the issues (i.e. topics, leaders, etc.) comprising the debate into binary opposing camps. Simultaneously, our algorithm scales both the blogs and the underlying issues on a univariate scale. Using this scale, a researcher can identify moderate and extreme blogs within each camp, and polarizing vs. unifying issues. Through performance evaluations we show that our proposed algorithm provides an effective solution to the problem, and performs much better than existing baseline algorithms adapted to solve this new problem. In our experiments, we used both real data from political blogosphere and US Congress records, as well as synthetic data which were obtained by varying polarization and degree distribution of the vertices of the graph to show the robustness of our algorithm.

Proceedings Fourth International Conference on Tools with Artificial Intelligence TAI '92, 1992

An algorithm suitable for the full transitive closure problem, which is used to solve uninstantia... more An algorithm suitable for the full transitive closure problem, which is used to solve uninstantiated recursive queries in deductive databases, is presented. In this algorithm there are two phases. In the first phase a general graph is condensed into an acyclic graph and at the same time a special sparse matrix is formed from the acyclic graph. The second phase

Proceedings of IEEE 9th International Conference on Data Engineering, 1993

T h e development of efficaeiii nlgoralhms io process the daffereni f o r m s of thc iransziiae-c... more T h e development of efficaeiii nlgoralhms io process the daffereni f o r m s of thc iransziiae-closurc (TC) queries wathzn the coiitezt of large datnbasf systems has recently aitracted a large uolzrme of research efforis. I n thas paper, we preseni a new algoraihni suziable f o r processang one of ihese fornts, ihe so called strong partially-instantiated, an which one of ihe query's argument as znstantzated io a set of constants and the processang of whzch yaelds a set of iiiples ihat draw t h e w values form both of the query's znstaiitzaied and unanstantzated arguments. Thas algoriihin avoads the redundant computaizons and the high siorage cost fotind an a number of simzlar algoraihnts. Crsaiig saniulataon, thas paper compares ihc performance of ihe n e w olgorzihm wiih those found i n liicraiiire and .shows clearly ihe superiorziy of ihe u e u , algoriihm.

The Computer Journal, 1966

IEEE Transactions on Knowledge and Data Engineering, 1996

Computers & Graphics, 1999

VLDB '02: Proceedings of the 28th International Conference on Very Large Databases, 2002

A workflow consists of a collection of coordinated tasks designed to carry out a welldefined comp... more A workflow consists of a collection of coordinated tasks designed to carry out a welldefined complex process, such as catalog ordering, trip planning, or a business process in an enterprise. Scheduling of workflows is a problem of finding a correct execution sequence for the workflow tasks, i.e., execution that obeys the constraints that embody the business logic of the workflow. Research on workflow scheduling has largely concentrated on temporal constraints, which specify correct ordering of tasks. Another important class of constraints-those that arise from resource allocation-has received relatively little attention in workflow modeling. Since typically resources are not limitless and cannot be shared, scheduling of a workflow execution involves decisions as to which resources to use and when. In this work, we present a framework for workflows whose correctness is given by a set of resource allocation constraints and develop techniques for scheduling such systems. Our framework integrates Concurrent Transaction Logic (CTR) with constraint logic programming (CLP), yielding a new logical formalism, which we call Concurrent Constraint Transaction Logic, or CCTR.